Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series

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.