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You searched for +publisher:"Delft University of Technology" +contributor:("Verwer, Sicco"). Showing records 1 – 22 of 22 total matches.

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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

 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)

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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

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)

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

 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)

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

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)

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

 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)

Subjects/Keywords: log analysis; log differencing; anomaly detection; state machines; software engineering; sequence alignment; model checkers; log comparison

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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

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)

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

 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)

Subjects/Keywords: semi-supervised learning; variational inference; deep learning; machine learning

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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

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)

Subjects/Keywords: Anomaly Detection; LSTM; Root cause analysis; ASML

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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

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)

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

 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)

Subjects/Keywords: state machines; blue-fringe; network threat detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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

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)

Subjects/Keywords: Train Maintenance Scheduling Problem; Reactive Agent; Supervised Learning; Neural Networks; Tree Search

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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

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)

Subjects/Keywords: Machine learning; Imbalanced learning; Network Theory

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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

 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)

Subjects/Keywords: anomaly detection; network analysis; security monitoring; monitoring ethics; clustering; network profiling

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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

 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)

Subjects/Keywords: scheduling; event-triggered control; timed automata; traffic abstractions; networked control systems; uppaal

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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

 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)

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

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)

Subjects/Keywords: Planning & scheduling algorithms; Machine learning; Local search algorithm; Imbalanced learning; Feature extraction

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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

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)

Subjects/Keywords: Malware families; Network Analysis; Sequence Clustering

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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

 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)

Subjects/Keywords: Scheduling; Maintenance scheduling; Resource Constrained Project Scheduling Problem; Precedence Constraint Posting; Machine learning; Monte Carlo simulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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

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)

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

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)

Subjects/Keywords: Log Analysis; Anomaly Detection; Machine Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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

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)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Outlier Detection

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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

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)

Subjects/Keywords: SSH Implementations; Fuzzing; State machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference 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

.