Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series

Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series

Muhammad Salman Khan (University of Manitoba, Winnipeg, Canada), Ken Ferens (Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada) and Witold Kinsner (Cognitive Systems Laboratory, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada & Telecommunications Research Laboratories (TRLabs), Winnipeg, Canada)
DOI: 10.4018/IJSSCI.2015070102
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Abstract

Growing global dependence over cyberspace has given rise to intelligent malicious threats due to increasing network complexities, inherent vulnerabilities embedded within the software and the limitations of existing cyber security systems to name a few. Malicious cyber actors exploit these vulnerabilities to carry out financial fraud, steal intellectual property and disrupt the delivery of essential online services. Unlike physical security, cyberspace is very difficult to secure due to the replacement of traditional computing platforms with sophisticated cloud computing and virtualization. These complex systems exhibit an increasing degree of complexity in tracking an attack or monitoring possible threats which is becoming intractable with the existing security firewalls and intrusion detection systems. In this paper, authors present a novel complexity detection technique using generalized multifractal singularity spectrum which is able to not only capture the growing complexity of the internet time series but also distinguishes the presence of an attack accurately.
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2. Cognitive Computing And Complexity

In the last few years, cognitive computing has emerged as a promising domain and has shown capabilities to outperform the traditional artificial intelligence tools including machine learning and neural networks. Cognitive computing is a combination of the techniques and tools which human use to infer and analyze objects. These include human decision making, mental inference, brain sciences and reasoning capabilities. All the traditional tools to analyze, predict and infer knowledge including but not limited to probability, statistics, dynamical systems, signal processing, machine learning, multiscale analysis and multifractal analysis are being used to define different aspects of cognitive computing. Primary goal of cognitive computing is to model improvements in making decision using experience from historical data (learning) and stimulus-response mechanism of human behavior to adapt according to the changing context and environments (adaptability). As opposed to traditional decision making computing systems and tools which aim to reduce complexity, cognitive computing aims to utilize the concepts of complexity and uncertainty to achieve tractability and robustness (Brasil, L. M., Mendes de Azevedo, F., Barreto, J. M., & Noirhomme-Fraiture, M.) (Kinsner, W. (2009, July-September)).

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