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You searched for +publisher:"University of New South Wales" +contributor:("Abbass, Hussein, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW"). Showing records 1 – 3 of 3 total matches.

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University of New South Wales

1. Dam, Hai Huong. A scalable evolutionary learning classifier system for knowledge discovery in stream data mining.

Degree: Information Technology & Electrical Engineering, 2008, University of New South Wales

Data mining (DM) is the process of finding patterns and relationships in databases.The breakthrough in computer technologies triggered a massive growth in datacollected and maintained by organisations. In many applications, these data arrivecontinuously in large volumes as a sequence of instances known as a data stream.Mining these data is known as stream data mining. Due to the large amount of dataarriving in a data stream, each record is normally expected to be processed onlyonce. Moreover, this process can be carried out on different sites in the organisationsimultaneously making the problem distributed in nature. Distributed stream datamining poses many challenges to the data mining community including scalabilityand coping with changes in the underlying concept over time.In this thesis, the author hypothesizes that learning classifier systems (LCSs) - aclass of classification algorithms - have the potential to work efficiently in distributedstream data mining. LCSs are an incremental learner, and being evolutionarybased they are inherently adaptive. However, they suffer from two main drawbacksthat hinder their use as fast data mining algorithms. First, they require a largepopulation size, which slows down the processing of arriving instances. Second,they require a large number of parameter settings, some of them are very sensitiveto the nature of the learning problem. As a result, it becomes difficult to choose aright setup for totally unknown problems.The aim of this thesis is to attack these two problems in LCS, with a specific focuson UCS - a supervised evolutionary learning classifier system. UCS is chosen as ithas been tested extensively on classification tasks and it is the supervised versionof XCS, a state of the art LCS.In this thesis, the architectural design for a distributed stream data mining systemwill be first introduced. The problems that UCS should face in a distributed datastream task are confirmed through a large number of experiments with UCS andthe proposed architectural design.To overcome the problem of large population sizes, the idea of using a NeuralNetwork to represent the action in UCS is proposed. This new system - called NLCS{ was validated experimentally using a small fixed population size and has showna large reduction in the population size needed to learn the underlying concept inthe data.An adaptive version of NLCS called ANCS is then introduced. The adaptive versiondynamically controls the population size of NLCS. A comprehensive analysis of thebehaviour of ANCS revealed interesting patterns in the behaviour of the parameters,which motivated an ensemble version of the algorithm with 9 nodes, each using adifferent parameter setting. In total they cover all patterns of behaviour noticed inthe system. A voting gate is used for the ensemble. The resultant ensemble doesnot require any parameter setting, and showed better performance on all datasetstested.The thesis concludes with testing the ANCS system in the architectural design fordistributed environments proposed earlier.The… Advisors/Committee Members: Abbass, Hussein, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW, Lokan, Chris, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW.

Subjects/Keywords: Data mining; Action map; Classification; Data stream; Neural network; Noisy data; Non-stationary environment; Reinforcement learning; Rule-based system; Static environment; Stream data mining; Supervised learning; Distributed data mining; Dynamic environment; Ensemble learning; Evolutionary computation; Genetic algorithm; Knowledge discovery; Learning classifier system; Negative correlation learning

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

APA (6th Edition):

Dam, H. H. (2008). A scalable evolutionary learning classifier system for knowledge discovery in stream data mining. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/38865 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3421/SOURCE1?view=true

Chicago Manual of Style (16th Edition):

Dam, Hai Huong. “A scalable evolutionary learning classifier system for knowledge discovery in stream data mining.” 2008. Doctoral Dissertation, University of New South Wales. Accessed October 22, 2019. http://handle.unsw.edu.au/1959.4/38865 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3421/SOURCE1?view=true.

MLA Handbook (7th Edition):

Dam, Hai Huong. “A scalable evolutionary learning classifier system for knowledge discovery in stream data mining.” 2008. Web. 22 Oct 2019.

Vancouver:

Dam HH. A scalable evolutionary learning classifier system for knowledge discovery in stream data mining. [Internet] [Doctoral dissertation]. University of New South Wales; 2008. [cited 2019 Oct 22]. Available from: http://handle.unsw.edu.au/1959.4/38865 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3421/SOURCE1?view=true.

Council of Science Editors:

Dam HH. A scalable evolutionary learning classifier system for knowledge discovery in stream data mining. [Doctoral Dissertation]. University of New South Wales; 2008. Available from: http://handle.unsw.edu.au/1959.4/38865 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3421/SOURCE1?view=true


University of New South Wales

2. Alam, Sameer. Evolving complexity towards risk : a massive scenario generation approach for evaluating advanced air traffic management concepts.

Degree: Information Technology & Electrical Engineering, 2008, University of New South Wales

Present day air traffic control is reaching its operational limits and accommodating future traffic growth will be a challenging task for air traffic service providers and airline operators. Free Flight is a proposed transition from a highly-structured and centrally-controlled air traffic system to a self-optimised and highly-distributed system. In Free Flight, pilots will have the flexibility of real-time trajectory planning and dynamic route optimisation given airspace constraints (traffic, weather etc.). A variety of advanced air traffic management (ATM) concepts are proposed as enabling technologies for the realisation of Free Flight. Since these concepts can be exposed to unforeseen and challenging scenarios in Free Flight, they need to be validated and evaluated in order to implement the most effective systems in the field. Evaluation of advanced ATM concepts is a challenging task due to the limitations in the existing scenario generation methodologies and limited availability of a common platform (air traffic simulator) where diverse ATM concepts can be modelled and evaluated. Their rigorous evaluation on safety metrics, in a variety of complex scenarios, can provide an insight into their performance, which can help improve upon them while developing new ones. In this thesis, I propose a non-propriety, non-commercial air traffic simulation system, with a novel representation of airspace, which can prototype advanced ATM concepts such as conflict detection and resolution, airborne weather avoidance and cockpit display of traffic information. I then propose a novel evolutionary computation methodology to algorithmically generate a massive number of conflict scenarios of increasing complexity in order to evaluate conflict detection algorithms. I illustrate the methodology in detail by quantitative evaluation of three conflict detection algorithms, from the literature, on safety metrics. I then propose the use of data mining techniques for the discovery of interesting relationships, that may exist implicitly, in the algorithm's performance data. The data mining techniques formulate the conflict characteristics, which may lead to algorithm failure, using if-then rules. Using the rule sets for each algorithm, I propose an ensemble of conflict detection algorithms which uses a switch mechanism to direct the subsequent conflict probes to an algorithm which is less vulnerable to failure in a given conflict scenario. The objective is to form a predictive model for algorithm's vulnerability which can then be included in an ensemble that can minimise the overall vulnerability of the system. In summary, the contributions of this thesis are: 1. A non-propriety, non-commercial air traffic simulation system with a novel representation of airspace for efficient modelling of advanced ATM concepts. 2. An Ant-based dynamic weather avoidance algorithm for traffic-constrained enroute airspace. 3. A novel representation of 4D air traffic scenario that allows the use of an evolutionary computation… Advisors/Committee Members: Abbass, Hussein, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW, Barlow, Michael, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW, Lindsay, Peter, University of Queensland.

Subjects/Keywords: Scenario planning; Free flight; Air traffic management; Conflict detection algorthims; Learning classifier systems; Weather avoidance; Genetic algorithms

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

APA (6th Edition):

Alam, S. (2008). Evolving complexity towards risk : a massive scenario generation approach for evaluating advanced air traffic management concepts. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/38966 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3534/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Alam, Sameer. “Evolving complexity towards risk : a massive scenario generation approach for evaluating advanced air traffic management concepts.” 2008. Doctoral Dissertation, University of New South Wales. Accessed October 22, 2019. http://handle.unsw.edu.au/1959.4/38966 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3534/SOURCE01?view=true.

MLA Handbook (7th Edition):

Alam, Sameer. “Evolving complexity towards risk : a massive scenario generation approach for evaluating advanced air traffic management concepts.” 2008. Web. 22 Oct 2019.

Vancouver:

Alam S. Evolving complexity towards risk : a massive scenario generation approach for evaluating advanced air traffic management concepts. [Internet] [Doctoral dissertation]. University of New South Wales; 2008. [cited 2019 Oct 22]. Available from: http://handle.unsw.edu.au/1959.4/38966 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3534/SOURCE01?view=true.

Council of Science Editors:

Alam S. Evolving complexity towards risk : a massive scenario generation approach for evaluating advanced air traffic management concepts. [Doctoral Dissertation]. University of New South Wales; 2008. Available from: http://handle.unsw.edu.au/1959.4/38966 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3534/SOURCE01?view=true


University of New South Wales

3. Shafi, Kamran. An online and adaptive signature-based approach for intrusion detection using learning classifier systems.

Degree: Information Technology & Electrical Engineering, 2008, University of New South Wales

This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, which are referred to as signatures, from network traffic through a supervised learning classifier system, UCS. The rules are learnt dynamically (i.e., using machine intelligence and without the requirement of a domain expert), and adaptively (i.e., as the data arrives without the need to relearn the complete model after presenting each data instance to the current model). Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt. The rule based profiling of normal behaviour allows for anomaly detection in that the events not matching any of the rules are considered potentially harmful and could be escalated for an action. We study the effect of key UCS parameters and operators on its performance and identify areas of improvement through this analysis. Several new heuristics are proposed that improve the effectiveness of UCS for the prediction of unseen and extremely rare intrusive activities. A signature extraction system is developed that adaptively retrieves signatures as they are discovered by UCS. The signature extraction algorithm is augmented by introducing novel subsumption operators that minimise overlap between signatures. Mechanisms are provided to adapt the main algorithm parameters to deal with online noisy and imbalanced class data. The performance of UCS, its variants and the signature extraction system is measured through standard evaluation metrics on a publicly available intrusion detection dataset provided during the 1999 KDD Cup intrusion detection competition. We show that the extended UCS significantly improves test accuracy and hit rate while significantly reducing the rate of false alarms and cost per example scores than the standard UCS. The results are competitive to the best systems participated in the competition in addition to our systems being online and incremental rule learners. The signature extraction system built on top of the extended UCS retrieves a magnitude smaller rule set than the base UCS learner without any significant performance loss. We extend the evaluation of our systems to real time network traffic which is captured from a university departmental server. A methodology is developed to build fully labelled intrusion detection dataset by mixing real background traffic with attacks simulated in a controlled environment. Tools are developed to pre-process the raw network data into feature vector format suitable for UCS and other related machine learning… Advisors/Committee Members: Abbass, Hussein, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW, Zhu, Weiping, Information Technology & Electrical Engineering, Australian Defence Force Academy, UNSW.

Subjects/Keywords: Classification; Intrusion detection; Evolutionary computation; Data mining; Genetic based machine learning; Supervised learning; Learning classifier system; Knowledge extraction

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

APA (6th Edition):

Shafi, K. (2008). An online and adaptive signature-based approach for intrusion detection using learning classifier systems. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/38991 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3559/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Shafi, Kamran. “An online and adaptive signature-based approach for intrusion detection using learning classifier systems.” 2008. Doctoral Dissertation, University of New South Wales. Accessed October 22, 2019. http://handle.unsw.edu.au/1959.4/38991 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3559/SOURCE01?view=true.

MLA Handbook (7th Edition):

Shafi, Kamran. “An online and adaptive signature-based approach for intrusion detection using learning classifier systems.” 2008. Web. 22 Oct 2019.

Vancouver:

Shafi K. An online and adaptive signature-based approach for intrusion detection using learning classifier systems. [Internet] [Doctoral dissertation]. University of New South Wales; 2008. [cited 2019 Oct 22]. Available from: http://handle.unsw.edu.au/1959.4/38991 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3559/SOURCE01?view=true.

Council of Science Editors:

Shafi K. An online and adaptive signature-based approach for intrusion detection using learning classifier systems. [Doctoral Dissertation]. University of New South Wales; 2008. Available from: http://handle.unsw.edu.au/1959.4/38991 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:3559/SOURCE01?view=true

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