Show simple item record

dc.contributor.supervisor Ferens, Ken (Electrical and Computer Engineering) en_US
dc.contributor.author Siddiqui, Sana
dc.date.accessioned 2017-06-28T15:42:45Z
dc.date.available 2017-06-28T15:42:45Z
dc.date.issued 2016-03 en_US
dc.date.issued 2017-05 en_US
dc.date.issued 2017 en_US
dc.date.issued 2017 en_US
dc.date.issued 2017-07 en_US
dc.identifier.citation Sana 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.citation Sana 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.citation Muhammad 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.citation Sana 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.citation Sana 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.uri http://hdl.handle.net/1993/32282
dc.description.abstract Application 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.publisher ACM (IWSPA) en_US
dc.publisher IEEE (IJCNN) en_US
dc.publisher Springer en_US
dc.publisher Springer en_US
dc.publisher IEEE (ICCI*CC) en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Artificial Neural Network en_US
dc.subject Classification en_US
dc.subject Multiscale en_US
dc.subject Cognitive Intelligence en_US
dc.subject Dimensionality en_US
dc.subject Wavelets en_US
dc.subject Machine Intelligence en_US
dc.subject Fractals en_US
dc.subject Multifractals en_US
dc.subject Hebbian Learning en_US
dc.subject Instance Based Learners en_US
dc.subject Complexity Analysis en_US
dc.subject Packet Captures en_US
dc.subject Network Threats en_US
dc.subject Malware Detection en_US
dc.subject Machine Learning en_US
dc.subject Computational Intelligence en_US
dc.subject Cognitive Computing en_US
dc.subject Cognitive Informatics en_US
dc.subject Cyber Kill Chain en_US
dc.subject Cyber Threat en_US
dc.subject Cyber Security en_US
dc.subject Obfuscated Cyber Threats en_US
dc.subject Advanced Indistinguishable Threats en_US
dc.title Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats en_US
dc.type info:eu-repo/semantics/masterThesis
dc.type master thesis en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Kinsner, Witold (Electrical and Computer Engineering) Wang, Yang (Computer Science) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2017 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

View Statistics