Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats

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dc.contributor.authorSiddiqui, Sana
dc.contributor.examiningcommitteeKinsner, Witold (Electrical and Computer Engineering) Wang, Yang (Computer Science)en_US
dc.contributor.supervisorFerens, Ken (Electrical and Computer Engineering)en_US
dc.date.accessioned2017-06-28T15:42:45Z
dc.date.available2017-06-28T15:42:45Z
dc.date.issued2016-03en_US
dc.date.issued2017-05en_US
dc.date.issued2017en_US
dc.date.issued2017en_US
dc.date.issued2017-07en_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractApplication of machine intelligence is severely challenged in the domain of cyber security due to the surreptitious nature of advanced cyber threats which are persistent and defy existing cyber defense mechanisms. Further, zero day attacks are also on the rise although many of these new attacks are merely a variant of an old and known threat. Machine enabled intelligence is limited in solving advanced and complex problems of detecting these mutated threats. This problem can be attributed to the single scale analysis nature of all the machine learning algorithms including but not limited to artificial neural networks, evolutionary algorithms, bio-inspired machine intelligence et al. This M.Sc. thesis addresses the challenge of detecting advanced cyber threats which conceal themselves under normal or benign activity. Three novel cognitive complexity analysis based algorithms have been proposed which modify the existing single scale machine learning algorithms by incorporating the notion of multiscale complexity in them. Particularly, network based threats are considered using two different publicly available data sets. Moreover, fractal and wavelet based multiscale analysis approach is incorporated in decision making backbone of k-Nearest Neighbours (k-NN) algorithm, Gradient Descent based Artificial Neural Network (ANN), and Hebbian learning algorithm. The classification performance of these algorithms is compared with their traditional single scale counterparts and an improvement in performance is observed consistently. This improvement is attributed to the usage of multiscale based complexity measures in the analysis of algorithm, features and error curve. The notion of multiscale evaluation reveals the hidden relationship which otherwise are averaged out when observed on a single scale. Also, the problem of class overlap which arises due to the stealth nature of cyber-attacks is addressed using the same concept. Conceptually, it is analogous of human cognitive capability employed in pattern discovery from complex objects based on their knowledge about how to connect and correlate various aspects together. It is imperative to note that this multiscale relationship should be a representative of the complexity measure of whole object so that it can characterize patterns based on various scales.en_US
dc.description.noteOctober 2017en_US
dc.identifier.citationSana Siddiqui, Muhammad Salman Khan, Ken Ferens and Witold Kinsner, "Detecting Advanced Persistent Threats using fractal dimension based machine learning classification," in proc. of the 2016 ACM Intl. Workshop on Security And Privacy Analytics (IWSPA), New Orleans, Louisiana, USA, Mar. 2016.en_US
dc.identifier.citationSana Siddiqui, Muhammad Salman Khan and Ken Ferens, "Multiscale Hebbian neural network for cyber threat detection," in proc. of 2017 IEEE Intl. Joint Conference on Neural Networks (IJCNN), May 2017.en_US
dc.identifier.citationMuhammad Salman Khan, Sana Siddiqui and Ken Ferens, "A cognitive and concurrent cyber kill chain model," in Computer and Network Security Essentials Book, Springer International Publishing AG, 2017.en_US
dc.identifier.citationSana Siddiqui, Muhammad Salman Khan and Ken Ferens, "Cognitive computing and multiscale analysis for cyber security," in Computer and Network Security Essentials Book, Springer International Publishing AG, 2017.en_US
dc.identifier.citationSana Siddiqui, Muhammad Salman Khan, Ken Ferens and Witold Kinsner, "Fractal based cognitive neural network to detect obfuscated and indistinguishable Internet threats," in proc. of IEEE Intl. Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), Jul. 2017.en_US
dc.identifier.urihttp://hdl.handle.net/1993/32282
dc.language.isoengen_US
dc.publisherACM (IWSPA)en_US
dc.publisherIEEE (IJCNN)en_US
dc.publisherSpringeren_US
dc.publisherSpringeren_US
dc.publisherIEEE (ICCI*CC)en_US
dc.rightsopen accessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectClassificationen_US
dc.subjectMultiscaleen_US
dc.subjectCognitive Intelligenceen_US
dc.subjectDimensionalityen_US
dc.subjectWaveletsen_US
dc.subjectMachine Intelligenceen_US
dc.subjectFractalsen_US
dc.subjectMultifractalsen_US
dc.subjectHebbian Learningen_US
dc.subjectInstance Based Learnersen_US
dc.subjectComplexity Analysisen_US
dc.subjectPacket Capturesen_US
dc.subjectNetwork Threatsen_US
dc.subjectMalware Detectionen_US
dc.subjectMachine Learningen_US
dc.subjectComputational Intelligenceen_US
dc.subjectCognitive Computingen_US
dc.subjectCognitive Informaticsen_US
dc.subjectCyber Kill Chainen_US
dc.subjectCyber Threaten_US
dc.subjectCyber Securityen_US
dc.subjectObfuscated Cyber Threatsen_US
dc.subjectAdvanced Indistinguishable Threatsen_US
dc.titleCognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threatsen_US
dc.typemaster thesisen_US
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