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dc.contributor.advisorBalakrishnan, N
dc.contributor.authorThomas, Ciza
dc.date.accessioned2010-12-31T06:49:48Z
dc.date.accessioned2018-07-31T05:08:51Z
dc.date.available2010-12-31T06:49:48Z
dc.date.available2018-07-31T05:08:51Z
dc.date.issued2010-12-31
dc.date.submitted2009-04
dc.identifier.urihttp://etd.iisc.ac.in/handle/2005/981
dc.description.abstractThe technique of sensor fusion addresses the issues relating to the optimality of decision-making in the multiple-sensor framework. The advances in sensor fusion enable to perform intrusion detection for both rare and new attacks. This thesis discusses this assertion in detail, and describes the theoretical and experimental work done to show its validity. The attack-detector relationship is initially modeled and validated to understand the detection scenario. The different metrics available for the evaluation of intrusion detection systems are also introduced. The usefulness of the data set used for experimental evaluation has been demonstrated. The issues connected with intrusion detection systems are analyzed and the need for incorporating multiple detectors and their fusion is established in this work. Sensor fusion provides advantages with respect to reliability and completeness, in addition to intuitive and meaningful results. The goal for this work is to investigate how to combine data from diverse intrusion detection systems in order to improve the detection rate and reduce the false-alarm rate. The primary objective of the proposed thesis work is to develop a theoretical and practical basis for enhancing the performance of intrusion detection systems using advances in sensor fusion with easily available intrusion detection systems. This thesis introduces the mathematical basis for sensor fusion in order to provide enough support for the acceptability of sensor fusion in performance enhancement of intrusion detection systems. The thesis also shows the practical feasibility of performance enhancement using advances in sensor fusion and discusses various sensor fusion algorithms, its characteristics and related design and implementation is-sues. We show that it is possible to build performance enhancement to intrusion detection systems by setting proper threshold bounds and also by rule-based fusion. We introduce an architecture called the data-dependent decision fusion as a framework for building intrusion detection systems using sensor fusion based on data-dependency. Furthermore, we provide information about the types of data, the data skewness problems and the most effective algorithm in detecting different types of attacks. This thesis also proposes and incorporates a modified evidence theory for the fusion unit, which performs very well for the intrusion detection application. The future improvements in individual IDSs can also be easily incorporated in this technique in order to obtain better detection capabilities. Experimental evaluation shows that the proposed methods have the capability of detecting a significant percentage of rare and new attacks. The improved performance of the IDS using the algorithms that has been developed in this thesis, if deployed fully would contribute to an enormous reduction of the successful attacks over a period of time. This has been demonstrated in the thesis and is a right step towards making the cyber space safer.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG23408en_US
dc.subjectSensor Networksen_US
dc.subjectIntrusion Detection Systemsen_US
dc.subjectDetector Networksen_US
dc.subjectDetectors (Computer Science)en_US
dc.subjectSensor Fusionen_US
dc.subjectData-Dependent Decision Fusionen_US
dc.subjectSensor Fusion Algorithmsen_US
dc.subjectDemspter-Shafer Evidence Theoryen_US
dc.subjectChebyshev Inequalityen_US
dc.subjectNeural Networken_US
dc.subjectAttack-Detectionen_US
dc.subjectData-dependent Decision Fusionen_US
dc.subject.classificationComputer Scienceen_US
dc.titlePerformance Enhancement Of Intrusion Detection System Using Advances In Sensor Fusionen_US
dc.typeThesisen_US
dc.degree.namePhDen_US
dc.degree.levelDoctoralen_US
dc.degree.disciplineFaculty of Engineeringen_US


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