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Delft University of Technology
1.
van der Lee, Wesley (author).
Vulnerability Detection in Mobile Applications Using State Machine Modeling.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:8699be26-b226-4c55-bf0a-fd290455cd57 
► Mobile applications play a critical role in modern society. Although mobile apps are widely adopted, everyday news shows that the applications often contain severe security…
(more)
▼ Mobile applications play a critical role in modern society. Although mobile apps are widely adopted, everyday news shows that the applications often contain severe security vulnerabilities. Recent work indicates that state machine learning has proven to be an effective method for vulnerability detection in software implementations. The state machine that can be learned about a software implementation provides additional insight into the internal software structure. The insight can then be used as input for security assessment which most of the times is performed by manual evaluation of the learned model. In this thesis, we aim to extend state machine learning to improve the security of mobile applications in an automated way, solving two problem. The first problem is the lack of a methodology to learn state machines for mobile apps. The second problem is the need for an approach that detects vulnerabilities from the inferred models. To the best of our knowledge, there exists no framework that automatically infers behavioral state machine models on mobile Android applications, nor does there exist a methodology for automatic vulnerability detection on the inferred models. We propose two solutions to the aforementioned problems. For the former, a framework for inferring a state machine model on general mobile Android applications is presented, which uses active state machine learning algorithms to ensure time optimization and model correctness on the learning process. For the latter, we designed algorithms that use the inferred models and determine the presence of vulnerabilities. We combine both solutions and propose a novel testing methodology that gains new insights into the behavior of an app and achieves the goal of vulnerability detection. The methodology identified relevant security weaknesses in numerous Android apps. Moreover, the solution can detect rogue applications such as a malicious WhatsApp version in the Android Play Store, which affected over a million devices in three days on November 2017.
Advisors/Committee Members: Verwer, Sicco (mentor), Delft University of Technology (degree granting institution).
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APA (6th Edition):
van der Lee, W. (. (2018). Vulnerability Detection in Mobile Applications Using State Machine Modeling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:8699be26-b226-4c55-bf0a-fd290455cd57
Chicago Manual of Style (16th Edition):
van der Lee, Wesley (author). “Vulnerability Detection in Mobile Applications Using State Machine Modeling.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:8699be26-b226-4c55-bf0a-fd290455cd57.
MLA Handbook (7th Edition):
van der Lee, Wesley (author). “Vulnerability Detection in Mobile Applications Using State Machine Modeling.” 2018. Web. 18 Jan 2021.
Vancouver:
van der Lee W(. Vulnerability Detection in Mobile Applications Using State Machine Modeling. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:8699be26-b226-4c55-bf0a-fd290455cd57.
Council of Science Editors:
van der Lee W(. Vulnerability Detection in Mobile Applications Using State Machine Modeling. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:8699be26-b226-4c55-bf0a-fd290455cd57
Delft University of Technology
2.
Etta Tabe, Takang Kajikaw (author).
Automated data exfiltration detection using netflow metadata.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:19aa873d-b38d-4133-bcf8-7c6c625af739
► The volume and sophistication of data exfiltration attacks over networks have significantly increased in the last decade. This has resulted in the need for defense…
(more)
▼ The volume and sophistication of data exfiltration attacks over networks have significantly increased in the last decade. This has resulted in the need for defense mechanisms, to effectively detect both known and unknown data exfiltration scenarios over the network. While methods such as DPI (Deep Packet Inspection) are commonly used to detect data exfiltrations, this mechanism requires a thorough inspection of every payload or packet going out of the network, making it unsuitable for use in some environments, as it is quite resource intensive and can lead to severe data privacy implications. In our work, we use lightweight netflows which are non-privacy invasive to detect data exfiltrations at connection-level granularity. The key intuition behind our proposed solution is that connections involved in data exfiltration tend to differentiate themselves from normal network connections based on certain feature values. The result of this research shows that features extracted from netflows such as the duration of a netflow, the source bytes, the source bytes sent per second, the source bytes sent per packet and the producer-consumer ratio can be used to effectively detect data exfiltration. Subsequently, connections are grouped using k-means, and the robust Z-score of their distances from their respective cluster centroid is used as a statistical and distance-based technique to detect connections involved in a data exfiltration. While this method detects some data exfiltration scenarios, it results in a significant number of false positives. Combining this with the results from the LOF (local outlier factor) and the LoOP (local outlier probability), which are density-based techniques, leads to a more robust model, as it significantly reduces the number of false positives and false negatives. Also, we show that using the smallest clusters formed from k-means for analysis leads to similar detection results as the entire datasets, with a significant reduction in computation time.
Cyber Security | Data science and technology
Advisors/Committee Members: Verwer, Sicco (mentor), Cooper, Peter (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Data exfiltration detection; netflows; local outlier probability; local outlier factor; anomaly detection; network traffic analysis
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APA (6th Edition):
Etta Tabe, T. K. (. (2019). Automated data exfiltration detection using netflow metadata. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:19aa873d-b38d-4133-bcf8-7c6c625af739
Chicago Manual of Style (16th Edition):
Etta Tabe, Takang Kajikaw (author). “Automated data exfiltration detection using netflow metadata.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:19aa873d-b38d-4133-bcf8-7c6c625af739.
MLA Handbook (7th Edition):
Etta Tabe, Takang Kajikaw (author). “Automated data exfiltration detection using netflow metadata.” 2019. Web. 18 Jan 2021.
Vancouver:
Etta Tabe TK(. Automated data exfiltration detection using netflow metadata. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:19aa873d-b38d-4133-bcf8-7c6c625af739.
Council of Science Editors:
Etta Tabe TK(. Automated data exfiltration detection using netflow metadata. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:19aa873d-b38d-4133-bcf8-7c6c625af739
Delft University of Technology
3.
Bao, Shiwei (author).
A Robust Solution to Train Shunting using Decision Trees.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101
► This research tackles the Train Unit Shunting Problem (TUSP) in train maintenance service sites. Many researches focus on producing feasible solutions, but only a few…
(more)
▼ This research tackles the Train Unit Shunting Problem (TUSP) in train maintenance service sites. Many researches focus on producing feasible solutions, but only a few of them concentrate on the robustness of solutions. In reality, it is preferred to generate robust plans against unpredictable disturbances. Besides, the approach is expected to replan if disturbances occur while performing the plan. We propose this Decision Tree (DT)-based sequential approach (DTS) that solves the TUSP by sequentially making a sub-decision according to the DT prediction. It generates solutions that are both feasible and robust. Furthermore, it operates fast using the pre-trained model. We conduct experiments and compare its performance with a heuristic algorithm and the Local Search algorithm (LS). The proposed approach DTS solves fewer problems than LS and the heuristic, but it outperforms others by generating more robust solutions.
Advisors/Committee Members: Verwer, Sicco (mentor), de Weerdt, Mathijs (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: train shunting; decision trees; Robust
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APA (6th Edition):
Bao, S. (. (2018). A Robust Solution to Train Shunting using Decision Trees. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101
Chicago Manual of Style (16th Edition):
Bao, Shiwei (author). “A Robust Solution to Train Shunting using Decision Trees.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101.
MLA Handbook (7th Edition):
Bao, Shiwei (author). “A Robust Solution to Train Shunting using Decision Trees.” 2018. Web. 18 Jan 2021.
Vancouver:
Bao S(. A Robust Solution to Train Shunting using Decision Trees. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101.
Council of Science Editors:
Bao S(. A Robust Solution to Train Shunting using Decision Trees. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101
Delft University of Technology
4.
Vos, Daniël (author).
Adversarially Robust Decision Trees Against User-Specified Threat Models.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002
► In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as…
(more)
▼ In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as decision trees are understandable, they suffer from adversarial attacks. When a decision tree is used to differentiate between a user's benign and malicious behavior, an adversarial attack allows the user to effectively evade the model by perturbing the inputs the model receives. We can use algorithms that take adversarial attacks into account to fit trees that are more robust. In this work we propose an algorithm that is two orders of magnitudes faster and scores 4.3% better on accuracy against adversaries moving all samples than the state-of-the-art work while accepting an intuitive and permissible threat model. Where previous threat models were limited to distance norms, we allow each feature to be perturbed with a user-specified threat model specifying either a maximum distance or constraints on the direction of perturbation. Additionally we introduce two hyperparameters rho and phi that can control the trade-off between accuracy vs robustness and accuracy vs fairness respectively. Using the hyperparameters we can train models with less than 5% difference in false positive rate between population groups while scoring on average 2.4% higher on accuracy against adversarial attacks. Lastly, we show that our decision trees perform similarly to more complex random forests of fair and robust decision trees.
Computer Science | Cyber Security
Advisors/Committee Members: Verwer, Sicco (mentor), Lagendijk, Inald (graduation committee), Loog, Marco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Adversarial Machine Learning; Decision Trees; Cyber Security
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APA (6th Edition):
Vos, D. (. (2020). Adversarially Robust Decision Trees Against User-Specified Threat Models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002
Chicago Manual of Style (16th Edition):
Vos, Daniël (author). “Adversarially Robust Decision Trees Against User-Specified Threat Models.” 2020. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002.
MLA Handbook (7th Edition):
Vos, Daniël (author). “Adversarially Robust Decision Trees Against User-Specified Threat Models.” 2020. Web. 18 Jan 2021.
Vancouver:
Vos D(. Adversarially Robust Decision Trees Against User-Specified Threat Models. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002.
Council of Science Editors:
Vos D(. Adversarially Robust Decision Trees Against User-Specified Threat Models. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002
Delft University of Technology
5.
Tsoni, Sofia (author).
Log Differencing using State Machines for Anomaly Detection.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:b0b39832-c921-412c-b6f8-9ac4c52b57f6
► Huge amounts of log data are generated every day by software. These data contain valuable information about the behavior and the health of the system,…
(more)
▼ Huge amounts of log data are generated every day by software. These data contain valuable information about the behavior and the health of the system, which is rarely exploited, because of their volume and unstructured nature. Manually going through log files is a time-consuming and labor-intensive procedure for developers. Nonetheless logging information can expose the problematic execution of the software, even though the final outcome seem to be normal. Nowadays the automatic analysis of the log files is crucial for detecting problems, but mainly for understanding how the software behaves, which would be beneficial for the prevention of failures and improvement of the software itself. Towards that direction, this project aims the identifications of unexpected executions of the software and the determination of the root cause behind them. In more details, the expected behavior of the software can be approximated using model inference techniques and the newly incoming observed data can be analyzed to verify if they are conformed by the expected behavior. The conformance checking method that will be used is called replay. The incoming traces will be replayed in the graph, at the point they are not validated, the alignment algorithm will take over. The sequence alignment is performed in three different ways. Two of the methods are looking for the best alignment at a specific radius around the problematic node. Additionally a global alignment technique is implemented, which is based on the famous algorithm by Needleman and Wunsch for DNA sequences. Our goal required the modification of the aforementioned algorithm to not only align two sequences, but a sequence with a tree structured model. Finally the implemented tool provides the visualization of the differences in a way that makes it intuitive for the developers to understand what went wrong. Some additional information are also provided to make the investigation of the "anomaly" easier.
Advisors/Committee Members: Verwer, Sicco (mentor), van Deursen, Arie (graduation committee), Finavaro Aniche, Mauricio (graduation committee), Wieman, Rick (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: log analysis; log differencing; anomaly detection; state machines; software engineering; sequence alignment; model checkers; log comparison
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APA ·
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APA (6th Edition):
Tsoni, S. (. (2019). Log Differencing using State Machines for Anomaly Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b0b39832-c921-412c-b6f8-9ac4c52b57f6
Chicago Manual of Style (16th Edition):
Tsoni, Sofia (author). “Log Differencing using State Machines for Anomaly Detection.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:b0b39832-c921-412c-b6f8-9ac4c52b57f6.
MLA Handbook (7th Edition):
Tsoni, Sofia (author). “Log Differencing using State Machines for Anomaly Detection.” 2019. Web. 18 Jan 2021.
Vancouver:
Tsoni S(. Log Differencing using State Machines for Anomaly Detection. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:b0b39832-c921-412c-b6f8-9ac4c52b57f6.
Council of Science Editors:
Tsoni S(. Log Differencing using State Machines for Anomaly Detection. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:b0b39832-c921-412c-b6f8-9ac4c52b57f6
Delft University of Technology
6.
Plaisant van der Wal, Renzo (author).
The Future of Fraud Detection: Detecting Fraudulent Insurance Claims Using Machine Learning Methods.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:935a0d46-2e26-4af5-b308-32b5fe54926b
► Machine learning methods are explored in an attempt to achieve better predictive performance than the legacy rule-based fraud detection systems that are currently used to…
(more)
▼ Machine learning methods are explored in an attempt to achieve better predictive performance than the legacy rule-based fraud detection systems that are currently used to detect fraudulent car insurance claims. There are two key principles that lead the exploration of machine learning techniques and algorithms in this thesis, namely, the applicability to imbalanced data, and the interpretability of predictions. The dataset used for model training and evaluation contains only 0.3% fraudulent claims compared to 99.7% non-fraudulent claims, which can therefore be considered highly imbalanced. Furthermore, prediction interpretability is of great importance, since fraud experts are directly interfacing with the output of the machine learning models. With the key principles in mind, this thesis considers four algorithms, Logistic Regression, Random Forest, LightGBM and a Stacking classifier. The algorithms are trained on the imbalanced learning problem by using a combination of undersampling (random and Edited Nearest Neighbors), oversampling (SMOTE) and class weighting. Conclusively, each trained model meets the objective, with the Stacking classifier combining the best performance with the lowest variance. By benchmarking the baseline for two different parameters, the models can be evaluated for two boundary conditions, which leads to tunable performance between the two conditions. Ultimately, the performance of the Stacking classifier is tunable (by moving its classification threshold) to roughly a 70-80% increase in extra fraud caught or a 75% reduction in effort. Extra fraud will increase the amount of real fraudulent claims that fraud experts get to see, and effort reduction leads to an increase in capacity, which enables fraud experts to spend more time on other more relevant tasks.
Computer Engineering
Advisors/Committee Members: Al-Ars, Zaid (mentor), Verwer, Sicco (graduation committee), de Voogd, G.W.H. (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Insurance; Machine Learning; fraud detection; fraud; imbalanced
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APA (6th Edition):
Plaisant van der Wal, R. (. (2018). The Future of Fraud Detection: Detecting Fraudulent Insurance Claims Using Machine Learning Methods. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:935a0d46-2e26-4af5-b308-32b5fe54926b
Chicago Manual of Style (16th Edition):
Plaisant van der Wal, Renzo (author). “The Future of Fraud Detection: Detecting Fraudulent Insurance Claims Using Machine Learning Methods.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:935a0d46-2e26-4af5-b308-32b5fe54926b.
MLA Handbook (7th Edition):
Plaisant van der Wal, Renzo (author). “The Future of Fraud Detection: Detecting Fraudulent Insurance Claims Using Machine Learning Methods.” 2018. Web. 18 Jan 2021.
Vancouver:
Plaisant van der Wal R(. The Future of Fraud Detection: Detecting Fraudulent Insurance Claims Using Machine Learning Methods. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:935a0d46-2e26-4af5-b308-32b5fe54926b.
Council of Science Editors:
Plaisant van der Wal R(. The Future of Fraud Detection: Detecting Fraudulent Insurance Claims Using Machine Learning Methods. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:935a0d46-2e26-4af5-b308-32b5fe54926b
Delft University of Technology
7.
Jurasiński, Karol (author).
Towards deeper understanding of semi-supervised learning with variational autoencoders.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb
► Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled…
(more)
▼ Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled data, which allowed the development of SSL models with the usage of deep neural networks. However, some of these models rely on ad-hoc loss additions for training, and have constraints on the latent space, which effectively prevent the use of recent developments in improving the posterior approximations. In this paper, we analyse the limitations of semi-supervised deep generative models based on VAEs, and show that it is possible to drop the assumptions made on the latent space. We present a simplified method for semi-supervised learning which combines the discriminative and generative loss in a principled manner. Our model allows for straightforward application of normalizing flows and achieves competitive results in semi-supervised classification tasks.
Advisors/Committee Members: Loog, Marco (mentor), Viering, Tom (mentor), Hung, Hayley (graduation committee), Verwer, Sicco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: semi-supervised learning; variational inference; deep learning; machine learning
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APA ·
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APA (6th Edition):
Jurasiński, K. (. (2019). Towards deeper understanding of semi-supervised learning with variational autoencoders. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb
Chicago Manual of Style (16th Edition):
Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.
MLA Handbook (7th Edition):
Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Web. 18 Jan 2021.
Vancouver:
Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.
Council of Science Editors:
Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb
Delft University of Technology
8.
SHI, XIAOTONG (author).
Anomaly detection and diagnosis in ASML event log using attentional LSTM network.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9
► In the ASML test system, all activity events of the test are continuously recorded in event logs, and these logs are intended to help people…
(more)
▼ In the ASML test system, all activity events of the test are continuously recorded in event logs, and these logs are intended to help people diagnose the root cause of a failure. However, due to the large scale of the logs, manual inspection of these logs consumes lots of effort and time, and the lack of expert knowledge of engineers makes the efficient diagnosis more difficult. To improve the failure diagnosis efficiency in ASML, this paper proposes an attentional long-short term neural network into log sequence analysis. The LSTM neural network extracts the underlying dependencies in the event log and an attention layer is appended after to measure the importance of earlier events on the prediction of future events. The model learns the normal patterns from a large number of event logs from successful tests and detects deviations from normal patterns as anomalies. The likelihood of being abnormal of an event is measured by how far it deviates from the prediction. And the prediction process of the model can be understood by visualizing the attention scores of earlier events when the model makes decisions. Moreover, a visualization tool is built to illustrate the locations of anomalies and interpret the causes of anomalies through the attention maps.
Computer Science
Advisors/Committee Members: Verwer, Sicco (mentor), Antonello, Mauro (mentor), Zaidman, Andy (graduation committee), van Gemert, Jan (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Anomaly Detection; LSTM; Root cause analysis; ASML
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
SHI, X. (. (2019). Anomaly detection and diagnosis in ASML event log using attentional LSTM network. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9
Chicago Manual of Style (16th Edition):
SHI, XIAOTONG (author). “Anomaly detection and diagnosis in ASML event log using attentional LSTM network.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9.
MLA Handbook (7th Edition):
SHI, XIAOTONG (author). “Anomaly detection and diagnosis in ASML event log using attentional LSTM network.” 2019. Web. 18 Jan 2021.
Vancouver:
SHI X(. Anomaly detection and diagnosis in ASML event log using attentional LSTM network. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9.
Council of Science Editors:
SHI X(. Anomaly detection and diagnosis in ASML event log using attentional LSTM network. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9
Delft University of Technology
9.
Morales Martinez, Francisco (author).
Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:c6c33089-3715-421c-ba24-48302ca708b3
► Weak baselines have been present in Information Retrieval (IR) for decades. They have been associated with IR progress stagnation, baseline selection bias to publish results…
(more)
▼ Weak baselines have been present in Information Retrieval (IR) for decades. They have been associated with IR progress stagnation, baseline selection bias to publish results more readily, and models’ effectiveness reproducibility issues that hinder the validation of results by independent research teams. Weak baselines have been studied by the IR community; however, the focus has been almost exclusive on ad-hoc retrieval, the most popular IR task, leaving outside other IR tasks and datasets recently de- veloped. Current deep neural IR research is particularly vulnerable to the issues with weak baselines due to the hype surrounding deep learning. In this thesis we investigate the cases of weak baselines in ad-hoc retrieval and question answering (QA), two representative IR tasks among 13 cases of weak baselines we found in current deep neural IR research from EMNLP 2018 conference. In particular, we study whether the recently introduced deep neural IR models are actually significantly more effective than the reported IR baselines or than LambdaMART, the Learning to Rank (LTR) model we propose plus hyperparameter optimization (HPO). We also benchmark two HPO methods: RS and BOHB, to determine which method is more efficient to retrieve a good hyperparameter configuration. Throughout our experiments we show that the effectiveness of the novel deep neural IR models can be difficult to replicate, it might be lower than reported, and that it is not necessarily significantly higher than the baseliness. Furthermore, we demonstrate that BOHB is more efficient than RS, but the HPO process not always improves the effectiveness of LambdaMART significantly.
Computer Science / Data Science and Technology
Advisors/Committee Members: Hauff, Claudia (mentor), Liem, Cynthia (graduation committee), Verwer, Sicco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Information Retrieval; Baselines; Learning to Rank; Deep Neural IR; Ad-hoc Retrieval; Question Answering
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APA (6th Edition):
Morales Martinez, F. (. (2020). Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c6c33089-3715-421c-ba24-48302ca708b3
Chicago Manual of Style (16th Edition):
Morales Martinez, Francisco (author). “Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering.” 2020. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:c6c33089-3715-421c-ba24-48302ca708b3.
MLA Handbook (7th Edition):
Morales Martinez, Francisco (author). “Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering.” 2020. Web. 18 Jan 2021.
Vancouver:
Morales Martinez F(. Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:c6c33089-3715-421c-ba24-48302ca708b3.
Council of Science Editors:
Morales Martinez F(. Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:c6c33089-3715-421c-ba24-48302ca708b3
Delft University of Technology
10.
Schouten, Hans (author).
Learning State Machines from data streams and an application in network-based threat detection.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:4aef512b-5c86-4ae0-b956-e3e9fa6aa966
► Our increasingly interconnected society poses large risks in terms of cyber security. With network traffic volumes increasing and systems becoming more connected, maintaining visibility on…
(more)
▼ Our increasingly interconnected society poses large risks in terms of cyber security. With network traffic volumes increasing and systems becoming more connected, maintaining visibility on IT networks is a challenging yet important task. In recent years the number of cyber threats have increased dramatically. Monitoring and threat detection are more essential than ever to stay in control in a growing threat landscape. The powerful properties of state machines and the similarities between network traffic and traces used to learn state machines makes this a promising approach. Current learning methods; however, maintain an intermediate data structure that is converted in a state machine after all data has been processed. The continuous nature of network traffic makes this conventional approach inapplicable. This study provides a solution by developing a method for learning State Machines on real-time data streams. The proposed algorithm, framework and implementation are generic and can be applied to any use case that benefits from learning state machines on data streams. This thesis explores one specific use case, which is the use of state machine fingerprints in network-based threat detection. A system is designed capable of learning state machines on real-time traffic channels. The proposed detection method is demonstrated to be highly effective in matching traffic from various malware types to pre-learned fingerprints. The work in this thesis forms a stepping stone to the development of a robust detection method, capable of detecting a variety of threats on network data with low false alarm rates.
Advisors/Committee Members: Verwer, Sicco (mentor), Spaan, Matthijs (graduation committee), Lokhorst, Nathalie (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: state machines; blue-fringe; network threat detection
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APA (6th Edition):
Schouten, H. (. (2018). Learning State Machines from data streams and an application in network-based threat detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4aef512b-5c86-4ae0-b956-e3e9fa6aa966
Chicago Manual of Style (16th Edition):
Schouten, Hans (author). “Learning State Machines from data streams and an application in network-based threat detection.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:4aef512b-5c86-4ae0-b956-e3e9fa6aa966.
MLA Handbook (7th Edition):
Schouten, Hans (author). “Learning State Machines from data streams and an application in network-based threat detection.” 2018. Web. 18 Jan 2021.
Vancouver:
Schouten H(. Learning State Machines from data streams and an application in network-based threat detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:4aef512b-5c86-4ae0-b956-e3e9fa6aa966.
Council of Science Editors:
Schouten H(. Learning State Machines from data streams and an application in network-based threat detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:4aef512b-5c86-4ae0-b956-e3e9fa6aa966
Delft University of Technology
11.
Zhong, Shijian (author).
Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972
► The Train Maintenance Scheduling Problem (TMSP) is a real-world problem that aims at complete maintenance tasks of trains by scheduling their activities on a service…
(more)
▼ The Train Maintenance Scheduling Problem (TMSP) is a real-world problem that aims at complete maintenance tasks of trains by scheduling their activities on a service site. Common methods of constructing optimal solutions to this problem are difficult as the problem consists of several highly-related sub-problems. Currently, NS is using a lo- cal search algorithm to provide solutions for the problem. However, it has several deficiencies such as solution randomness and lacking flexibility for rescheduling. In this research, we investigated the applicability of sequential decision making and supervised learning for solving TMSP. First, we formulate the TMSP problem with a reactive sequential mechanism and define the state and action space. Next, we design a feature representation for states and come up with the best kind of neural network structure through comparisons. Then, we conduct experiments to compare several search strategies with the trained network as the heuristic and find the best one. Fi- nally, we evaluate the solvability of our system and conclude that our approach has a certain capability for solving small-scale problems.
Computer Science
Advisors/Committee Members: Verwer, Sicco (mentor), de Weerdt, Mathijs (mentor), Lee, Wan-Jui (mentor), van Gemert, Jan (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Train Maintenance Scheduling Problem; Reactive Agent; Supervised Learning; Neural Networks; Tree Search
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APA (6th Edition):
Zhong, S. (. (2018). Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972
Chicago Manual of Style (16th Edition):
Zhong, Shijian (author). “Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972.
MLA Handbook (7th Edition):
Zhong, Shijian (author). “Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search.” 2018. Web. 18 Jan 2021.
Vancouver:
Zhong S(. Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972.
Council of Science Editors:
Zhong S(. Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972
Delft University of Technology
12.
Shen, Xiwei (author).
Predicting vulnerable files by using machine learning method.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:899729ed-9b81-4973-a46a-18eca3131c8a
► Web applications have been gaining increased popularity around the globe, in such a way that a growing number of users are attracted to make use…
(more)
▼ Web applications have been gaining increased popularity around the globe, in such a way that a growing number of users are attracted to make use of the functionality and information provided by these applications. While providing solutions to complicated problems in a fast and reliable way is one of the most advantages of using web applications, these platforms can cause adverse effect on user’s life if controlled in unauthorized way by malicious people. A platform with more vulnerabilities are more likely to be attacked. This research is focusing on building a prediction model for detecting vulnerabilities of web applications at eBay. Based on the analysis of important features, we dig deeper to find decisive factors of web application vulnerabilities. Making use of data on GitHub, we extract features related to source code files and developer networks, such as modification frequency, number of involved developers and duration between two commits. By applying machine learning techniques in the field of vulnerability prediction, we are able to provide reasonable suggestions for developers in the beginning phase. This can help develop relative defect-free and well-documented software. In this paper, we will explain the prediction model in detail from the aspects of code complexity, developers' behaviors and their networks. Moreover, according to results of various classifiers, we offer possible causes of vulnerabilities and reasonable suggestions for avoiding vulnerabilities in the future. To conclude, main contributions of this thesis are valuable feature engineering, the machine learning model and applicable suggestions for predicting vulnerabilities effectively at eBay.
Computer Science
Advisors/Committee Members: Verwer, Sicco (mentor), Hartel, Pieter (graduation committee), Finavaro Aniche, Mauricio (graduation committee), Sedghi, Saeed (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Machine learning; Imbalanced learning; Network Theory
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APA (6th Edition):
Shen, X. (. (2018). Predicting vulnerable files by using machine learning method. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:899729ed-9b81-4973-a46a-18eca3131c8a
Chicago Manual of Style (16th Edition):
Shen, Xiwei (author). “Predicting vulnerable files by using machine learning method.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:899729ed-9b81-4973-a46a-18eca3131c8a.
MLA Handbook (7th Edition):
Shen, Xiwei (author). “Predicting vulnerable files by using machine learning method.” 2018. Web. 18 Jan 2021.
Vancouver:
Shen X(. Predicting vulnerable files by using machine learning method. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:899729ed-9b81-4973-a46a-18eca3131c8a.
Council of Science Editors:
Shen X(. Predicting vulnerable files by using machine learning method. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:899729ed-9b81-4973-a46a-18eca3131c8a
Delft University of Technology
13.
Mairet, Valentine (author).
Project Mapyen: A network analysis tool to identify anomalous host behaviours.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:60d6e300-41c5-4b5b-a3b9-3fccb5afce77
► The evolution of the cyber threat landscape drives companies towards state-of-the-art security monitoring techniques. Adyen, a payment service provider company, has both legal and moral…
(more)
▼ The evolution of the cyber threat landscape drives companies towards state-of-the-art security monitoring techniques. Adyen, a payment service provider company, has both legal and moral obligations to perform security monitoring within the company to remain an ethical and sustainable business. The challenge is to uncover a well-founded solution to detect real-time incidents using lightweight network traffic metadata. This research identifies an optimal clustering solution to perform anomaly detection on the logged network metadata and enhances the analysis using individual probability-based network profiles for each host. The proof of concept implemented for this research is called Mapyen, and it is validated against three different attack scenarios, namely port scans, malware infection simulations, and data exfiltration scenarios. Despite the low precision and recall scores of the initial Mapyen system, it shows great potential for future security research and development.
Advisors/Committee Members: Verwer, Sicco (mentor), van Deursen, Arie (graduation committee), Finavaro Aniche, Maurício (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: anomaly detection; network analysis; security monitoring; monitoring ethics; clustering; network profiling
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APA ·
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Export
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APA (6th Edition):
Mairet, V. (. (2018). Project Mapyen: A network analysis tool to identify anomalous host behaviours. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:60d6e300-41c5-4b5b-a3b9-3fccb5afce77
Chicago Manual of Style (16th Edition):
Mairet, Valentine (author). “Project Mapyen: A network analysis tool to identify anomalous host behaviours.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:60d6e300-41c5-4b5b-a3b9-3fccb5afce77.
MLA Handbook (7th Edition):
Mairet, Valentine (author). “Project Mapyen: A network analysis tool to identify anomalous host behaviours.” 2018. Web. 18 Jan 2021.
Vancouver:
Mairet V(. Project Mapyen: A network analysis tool to identify anomalous host behaviours. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:60d6e300-41c5-4b5b-a3b9-3fccb5afce77.
Council of Science Editors:
Mairet V(. Project Mapyen: A network analysis tool to identify anomalous host behaviours. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:60d6e300-41c5-4b5b-a3b9-3fccb5afce77
Delft University of Technology
14.
Schalkwijk, Paul (author).
Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:6ae619f2-9247-4c30-9710-b1ddf362896d
► As the use of Networked Control Systems increases, the need for control methods with more efficient network usage also grows. These methods require a more…
(more)
▼ As the use of Networked Control Systems increases, the need for control methods with more efficient network usage also grows. These methods require a more sophisticated way of pre- dicting their traffic, and an approach for this is using a formal modelling approach using Timed Automata. Timed Automata have been used for over 25 years for several scheduling problems, but have not been adopted by the control systems community for scheduling event- triggered systems. This is a recent development for which no easy to use software tools have been developed, and performance in real-world applications is yet untested. In this master thesis, an existing approach for scheduling event-triggered controllers is implemented in a set of tools. This approach creates abstractions of communication traffic, models them as timed automata and finds a scheduler avoiding communication conflicts. This set of tools is used to test the scalability with respect to abstraction accuracy and number of systems that can be connected. The set of tools can be used in the future to further improve on the techniques used.
Advisors/Committee Members: Mazo Espinosa, Manuel (mentor), de Albuquerque Gleizer, Gabriel (graduation committee), Verwer, Sicco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: scheduling; event-triggered control; timed automata; traffic abstractions; networked control systems; uppaal
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APA ·
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Export
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APA (6th Edition):
Schalkwijk, P. (. (2019). Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6ae619f2-9247-4c30-9710-b1ddf362896d
Chicago Manual of Style (16th Edition):
Schalkwijk, Paul (author). “Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:6ae619f2-9247-4c30-9710-b1ddf362896d.
MLA Handbook (7th Edition):
Schalkwijk, Paul (author). “Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata.” 2019. Web. 18 Jan 2021.
Vancouver:
Schalkwijk P(. Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:6ae619f2-9247-4c30-9710-b1ddf362896d.
Council of Science Editors:
Schalkwijk P(. Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:6ae619f2-9247-4c30-9710-b1ddf362896d
Delft University of Technology
15.
Anker, Eva (author).
Runtime analysis of Android apps based on their behaviour.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:78cf1391-4767-4027-803b-b2ad3bd583eb
► In the modern world, Smartphones are everywhere and Android is the most used operating system. To protect these devices against malicious actions, the behaviour of…
(more)
▼ In the modern world, Smartphones are everywhere and Android is the most used operating system. To protect these devices against malicious actions, the behaviour of Android apps needs to be studied. Current tooling does not provide complete insight into the behaviour of an Android app. A tool was built to observe what goes on inside an Android app. The tool can hook all functions and change the outcome of a function call. It is possible to log all method calls to observe when a method is called, with their arguments and return values. Every call the app makes inside the JVM can be shown and a complete picture of the application can be obtained. During this process the app stays responsive and will not slow down significantly. The information provided can be used for building a call graph, finding vulnerabilities or checking for app detection mechanisms.
Advisors/Committee Members: Verwer, Sicco (mentor), Zaidman, Andy (graduation committee), Valk, Kevin (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Android; Smartphones; JNI; Apps; Call graph; Vulnerability detection; Dynamic analysis; Dynamic Binary Instrumentation
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APA (6th Edition):
Anker, E. (. (2020). Runtime analysis of Android apps based on their behaviour. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:78cf1391-4767-4027-803b-b2ad3bd583eb
Chicago Manual of Style (16th Edition):
Anker, Eva (author). “Runtime analysis of Android apps based on their behaviour.” 2020. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:78cf1391-4767-4027-803b-b2ad3bd583eb.
MLA Handbook (7th Edition):
Anker, Eva (author). “Runtime analysis of Android apps based on their behaviour.” 2020. Web. 18 Jan 2021.
Vancouver:
Anker E(. Runtime analysis of Android apps based on their behaviour. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:78cf1391-4767-4027-803b-b2ad3bd583eb.
Council of Science Editors:
Anker E(. Runtime analysis of Android apps based on their behaviour. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:78cf1391-4767-4027-803b-b2ad3bd583eb
Delft University of Technology
16.
Dai, Lu (author).
A machine learning approach for optimisation in railway planning.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:20170a8c-7e1e-434f-b5d6-9ba933e2ab6e
► Planning and scheduling problem is a hard problem, especially in real life cases. The time and space complexity increase quickly along with the increase of…
(more)
▼ Planning and scheduling problem is a hard problem, especially in real life cases. The time and space complexity increase quickly along with the increase of problem size. In transportation systems, such problems exist a lot. The automation of transportation systems depends a lot on the improvements of developing planning and scheduling algorithm. Nowadays, machine learning, as modern technology, has been adopted in every field. The power of machine learning is its ability to obtain useful information from large datasets. Considering problem instance information and corresponding solution status as data and label respectively, there is the possibility that the solvable instances hold patterns in common. This is where machine learning comes into the stage. This research is focusing on how to combine machine learning with traditional planning and scheduling algorithm based on the Dutch Railway System. Specifically, making use of a large amount of stored instance and solution details to improve the service site planning process is our purpose. Combing machine learning and traditional scheduling system is a new and hard topic. In this work, we implement a machine learning system adapting to the scheduling algorithm content. We define and collect dataset matching our research goals. A framework is designed according to the imbalanced characteristics of our datasets. To explore the nature of algorithms, we choose to calculate features from the problem instances and scheduling process directly. Besides, we perform a high vol- ume of experiments to select the based machine learning techniques to adopt. What’s more, we also design a test framework to evaluate our machine learning systems. Im- provements are observed in our work. Additionally, we would like to explore the features and machine learning techniques to improve the performance in the future work.
Computer Engineering | Algorithmics
Advisors/Committee Members: Verwer, Sicco (mentor), de Weerdt, Mathijs (graduation committee), Tax, David (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Planning & scheduling algorithms; Machine learning; Local search algorithm; Imbalanced learning; Feature extraction
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Export
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APA (6th Edition):
Dai, L. (. (2018). A machine learning approach for optimisation in railway planning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:20170a8c-7e1e-434f-b5d6-9ba933e2ab6e
Chicago Manual of Style (16th Edition):
Dai, Lu (author). “A machine learning approach for optimisation in railway planning.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:20170a8c-7e1e-434f-b5d6-9ba933e2ab6e.
MLA Handbook (7th Edition):
Dai, Lu (author). “A machine learning approach for optimisation in railway planning.” 2018. Web. 18 Jan 2021.
Vancouver:
Dai L(. A machine learning approach for optimisation in railway planning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:20170a8c-7e1e-434f-b5d6-9ba933e2ab6e.
Council of Science Editors:
Dai L(. A machine learning approach for optimisation in railway planning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:20170a8c-7e1e-434f-b5d6-9ba933e2ab6e
Delft University of Technology
17.
Nadeem, Azqa (author).
Clustering Malware's Network Behavior using Simple Sequential Features.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:c8a221b9-9289-4978-a356-af64d8f2c5e0
► Developing malware variants is extremely cheap for attackers because of the availability of various obfuscation tools. These variants can be grouped in malware families, based…
(more)
▼ Developing malware variants is extremely cheap for attackers because of the availability of various obfuscation tools. These variants can be grouped in malware families, based on information retrieved from their static and dynamic analysis. Dynamic, network-level analysis of malware shows its core behavior since it captures the interaction with its developer. On the other hand, increasingly more emphasis is given to using Deep Packet Inspection (DPI) in order to cluster malware’s network behavior. However, DPI has severe privacy implications, as it involves inspecting payloads of the network traffic. This thesis presents an exploratory study, the aim of which is to characterize and cluster malware behavior using high-level, non-privacy-invasive, sequential features extracted from its network activity. The key intuition behind the proposed solution is that if the underlying infrastructure of distinct malware samples is similar, the order in which they perform certain actions should also be similar. The results of this research show that sequence clustering allows flexible and robust clusters, as opposed to using non-sequential features. The clusters themselves reveal interesting attacking capabilities, such as port scans, and the same Command and Control server responding to different malware families. Lastly, a comparison with clusters obtained from static analysis reveals that network-based clustering is far more qualified to determine the many behaviors exhibited by a single malware family, as well as behaviors common across multiple malware families.
Data Science & Technology | Cyber Security
Advisors/Committee Members: Verwer, Sicco (mentor), Hernandez Ganan, Carlos (mentor), Al-Ars, Zaid (graduation committee), Hartel, Pieter (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Malware families; Network Analysis; Sequence Clustering
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Manager
APA (6th Edition):
Nadeem, A. (. (2018). Clustering Malware's Network Behavior using Simple Sequential Features. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c8a221b9-9289-4978-a356-af64d8f2c5e0
Chicago Manual of Style (16th Edition):
Nadeem, Azqa (author). “Clustering Malware's Network Behavior using Simple Sequential Features.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:c8a221b9-9289-4978-a356-af64d8f2c5e0.
MLA Handbook (7th Edition):
Nadeem, Azqa (author). “Clustering Malware's Network Behavior using Simple Sequential Features.” 2018. Web. 18 Jan 2021.
Vancouver:
Nadeem A(. Clustering Malware's Network Behavior using Simple Sequential Features. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:c8a221b9-9289-4978-a356-af64d8f2c5e0.
Council of Science Editors:
Nadeem A(. Clustering Malware's Network Behavior using Simple Sequential Features. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:c8a221b9-9289-4978-a356-af64d8f2c5e0
Delft University of Technology
18.
Verburg, Floris (author).
Improving RCPSP algorithms using machine learning methods.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:82c2c44b-c3d6-4d9a-9b2f-49a0f71eb1a3
► For performing technical maintenance, it is important to keep a detailed schedule of resources and temporal constraints. The Resource Constrained Project Scheduling Problem (RCPSP) is…
(more)
▼ For performing technical maintenance, it is important to keep a detailed schedule of resources and temporal constraints. The Resource Constrained Project Scheduling Problem (RCPSP) is a well de- fined scheduling model with both resources and temporal constraints. Precedence Constraint Posting (PCP) is a technique to solve the NP-hard RCPSP problem, that currently uses heuristics for making decisions for selecting and resolving conflicts. Our work focuses on improving the quality of the solutions for PCP by replacing these heuristics by a machine learning classifier. In this work, several datasets are generated that are used for training classifiers. The performance of the PCP solver when replacing the heuristics with these classifiers is comparable with the performance of the solver when using heuristics, but on average it is slightly worse than the best performing heuristics. After implement- ing Monte Carlo simulation, we concluded that there was a slight, but statistically significant decrease in average makespan when using the machine learning classifiers for simulating the behaviour of the solver compared to the average makespan when using random heuristics for simulating the behaviour of the solver. However, future research is needed to further improve the performance of the machine learning classifiers, for which we propose a list of improvements based on our observations.
Advisors/Committee Members: de Weerdt, Mathijs (mentor), Yorke-Smith, Neil (graduation committee), Verwer, Sicco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Scheduling; Maintenance scheduling; Resource Constrained Project Scheduling Problem; Precedence Constraint Posting; Machine learning; Monte Carlo simulation
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APA (6th Edition):
Verburg, F. (. (2018). Improving RCPSP algorithms using machine learning methods. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:82c2c44b-c3d6-4d9a-9b2f-49a0f71eb1a3
Chicago Manual of Style (16th Edition):
Verburg, Floris (author). “Improving RCPSP algorithms using machine learning methods.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:82c2c44b-c3d6-4d9a-9b2f-49a0f71eb1a3.
MLA Handbook (7th Edition):
Verburg, Floris (author). “Improving RCPSP algorithms using machine learning methods.” 2018. Web. 18 Jan 2021.
Vancouver:
Verburg F(. Improving RCPSP algorithms using machine learning methods. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:82c2c44b-c3d6-4d9a-9b2f-49a0f71eb1a3.
Council of Science Editors:
Verburg F(. Improving RCPSP algorithms using machine learning methods. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:82c2c44b-c3d6-4d9a-9b2f-49a0f71eb1a3
Delft University of Technology
19.
MANGANAHALLI JAYAPRAKASH, Sandesh (author).
Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5
► The usage of Internet of Things (IoT) devices has been exponentially increasing and their security is often overlooked. Hackers exploit the vulnerabilities present to perform…
(more)
▼ The usage of Internet of Things (IoT) devices has been exponentially increasing and their security is often overlooked. Hackers exploit the vulnerabilities present to perform large scale attacks as well as to obtain privacy-sensitive information. Resource constraints combined with a lack of incentives for manufacturers makes it harder to implement security solutions part of these devices. This thesis aims at developing a system that monitors the behaviour of these IoT devices. Network traffic is captured and analysed as part of a network middle-box to model the behaviour of an IoT device. This traffic shows the interactions of the IoT device with other devices and hosts. By modelling the normal behaviour of a device, we can detect anomalies exhibited. Denial of Service attack was performed to evaluate the effectiveness of state machines in detecting anomalies. To verify the validity of state machines built based on network traffic in a laboratory setup, a test environment with a different setting was used. Traffic was captured from a smart home setting and used to validate the state machines. We show that state machines can be effectively used to model the behaviour of IoT devices at the packet level and can also be used to uniquely identify commands issued from smartphone to IoT device. They can also effectively distinguish attack traffic from normal traffic.
Computer Science | Cyber Security
Advisors/Committee Members: Verwer, Sicco (mentor), Nadeem, Azqa (mentor), Katsifodimos, Asterios (graduation committee), van der Lubbe, Jan (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Anomaly Detection; state machines; IoT
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APA (6th Edition):
MANGANAHALLI JAYAPRAKASH, S. (. (2019). Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5
Chicago Manual of Style (16th Edition):
MANGANAHALLI JAYAPRAKASH, Sandesh (author). “Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5.
MLA Handbook (7th Edition):
MANGANAHALLI JAYAPRAKASH, Sandesh (author). “Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices.” 2019. Web. 18 Jan 2021.
Vancouver:
MANGANAHALLI JAYAPRAKASH S(. Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5.
Council of Science Editors:
MANGANAHALLI JAYAPRAKASH S(. Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5
Delft University of Technology
20.
Lan, Yikai (author).
Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8
► Monitoring the release logs of modern online software is a challenging topic because of the enormous amount of release logs and the complicated release process.…
(more)
▼ Monitoring the release logs of modern online software is a challenging topic because of the enormous amount of release logs and the complicated release process. The goal of this thesis is to develop a pipeline that can monitor the release logs and find anomalous logs, automating this step with anomaly detection and reducing the required manual effort. We improve the pipeline from the recent work of Microsoft, enabling it to monitor logs with different severity levels and extremely long sequences. We first use IPLoM and its reconciling step for raw logs to obtain log events and then use log event sets, a simplified version of log sequences, for anomaly detection. The outlier scores of log event sets are calculated using anomaly detection algorithms, and those with an outlier score higher than the threshold are clustered to reduce the number of output. In the final output result, we propose two ranking functions to sort the potential anomalous clusters and only show the top 10 results. Another complementary step beside anomaly detection is designed to capture recurrent anomalies in known clusters that have seen before. By finding the optimal parameters for hierarchical clustering, nearest neighbor distance, and LOF, we test the performance of pipeline on Adyen log data and make our suggestions. Finally, we also test the robustness of the pipeline with two types of artificial data sets.
Computer Science
Software Technology
Advisors/Committee Members: van Deursen, Arie (mentor), Verwer, Sicco (mentor), Tax, David (graduation committee), Huibers, Pieter (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Log Analysis; Anomaly Detection; Machine Learning
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APA ·
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APA (6th Edition):
Lan, Y. (. (2018). Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8
Chicago Manual of Style (16th Edition):
Lan, Yikai (author). “Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8.
MLA Handbook (7th Edition):
Lan, Yikai (author). “Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection.” 2018. Web. 18 Jan 2021.
Vancouver:
Lan Y(. Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8.
Council of Science Editors:
Lan Y(. Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8
Delft University of Technology
21.
Liu, Xin (author).
Unsupervised Cross Domain Image Matching with Outlier Detection.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73
► This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the…
(more)
▼ This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the same time. This matching problem is made difficult by i) the different domain images that are related but under different conditions (e.g. photos of the same location captured in different illuminations), ii) unsupervised settings with paired-image information available only for one of the domains, iii) the existing of outliers that makes the two domains not fully overlap. To this end, we propose an end-to-end architecture that can match cross domain images in an unsupervised manner and handle not fully overlapping domains by outlier detection. Our architecture is composed of three subnetworks, two of which are fed with pairs of source images to learn the ”match” information. The other subnetwork is fed with target images, and works together with the other two subnetworks to learn domain invariant representations of the source samples and the target inlier samples by applying a weighted multi-kernel Maximum Mean Discrepancy (weighted MK-MMD). We propose the weighted MK-MMD, together with an entropy loss, for outlier detection. The entropy loss iteratively outputs the probability of a target sample to be an inlier during training. And the probabilities are used as weights in our weighted MK-MMD for aligning only the target inlier samples with the source samples. Extensive experimental evidence on Office [26] dataset and our proposed datasets Shape, Pitts-CycleGAN shows that the proposed approach yields state-of-the-art cross domain image matching and outlier detection performance on different benchmarks.
Pattern Recognition and Bioinformatics
Advisors/Committee Members: van Gemert, Jan (mentor), Khademi, Seyran (mentor), Reinders, Marcel (graduation committee), Verwer, Sicco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Outlier Detection
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APA ·
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MLA ·
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Manager
APA (6th Edition):
Liu, X. (. (2018). Unsupervised Cross Domain Image Matching with Outlier Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73
Chicago Manual of Style (16th Edition):
Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.
MLA Handbook (7th Edition):
Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Web. 18 Jan 2021.
Vancouver:
Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.
Council of Science Editors:
Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73
Delft University of Technology
22.
Yan, Yuzhu (author).
SSH Implementations: State Machine Learning and Analysis.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:8c807ce9-0ad6-4525-b7f3-c0271448040d
► Analyzing large cryptographic protocol implementations can be challenging since their implementations do not perfectly match the standard [6]. The popular, highly configurable remote login method,…
(more)
▼ Analyzing large cryptographic protocol implementations can be challenging since their implementations do not perfectly match the standard [6]. The popular, highly configurable remote login method, Secure Shell (SSH) is such an example. In this thesis, we researched the fuzzing methodologies for SSH implementations. Three tools (Backfuzz, Paramiko-sshfuzz and Protocol state fuzzing) were implemented to explore their capabilities and to determine the most effective one. The protocol state fuzzing technique resulted to be the most promising approach since it is well-developed and has recently revealed a few abnormal behaviours of SSH [6], moreover it is also actively used in several cryptographic protocol implementations (i.e. TLS). Consequently, we applied this method on an real SSH implementation, the OpenSSH library (OpenSSH6.7-p1). The results are analyzed against the source code and RFC standards. To solve the readability problem of the results caused by the complex architecture of the SSH protocol, we combined the obtained SSH state machine with D3.js data visualization technique. As a result, we developed a tool for debugging SSH implementations based on the protocol state fuzzing, code review and D3.js. Lastly, the utility tool is evaluated in a survey and future works are presented.
Cyber Security
Advisors/Committee Members: Verwer, Sicco (mentor), Amzucu, Dragos (graduation committee), van der Lubbe, Jan (graduation committee), Bozzon, Alessandro (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: SSH Implementations; Fuzzing; State machine learning
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APA ·
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MLA ·
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Export
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Manager
APA (6th Edition):
Yan, Y. (. (2017). SSH Implementations: State Machine Learning and Analysis. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:8c807ce9-0ad6-4525-b7f3-c0271448040d
Chicago Manual of Style (16th Edition):
Yan, Yuzhu (author). “SSH Implementations: State Machine Learning and Analysis.” 2017. Masters Thesis, Delft University of Technology. Accessed January 18, 2021.
http://resolver.tudelft.nl/uuid:8c807ce9-0ad6-4525-b7f3-c0271448040d.
MLA Handbook (7th Edition):
Yan, Yuzhu (author). “SSH Implementations: State Machine Learning and Analysis.” 2017. Web. 18 Jan 2021.
Vancouver:
Yan Y(. SSH Implementations: State Machine Learning and Analysis. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 18].
Available from: http://resolver.tudelft.nl/uuid:8c807ce9-0ad6-4525-b7f3-c0271448040d.
Council of Science Editors:
Yan Y(. SSH Implementations: State Machine Learning and Analysis. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:8c807ce9-0ad6-4525-b7f3-c0271448040d
. 
Open Access Theses and Dissertations