Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent

Abstract

Internet traffic monitoring is a crucial task for the security and reliability of communication networks and Internet of Things (IoT) infrastructure. This description of the traffic statistics is used to detect traffic anomalies. Nowadays, intruders and cybercriminals use different techniques to bypass existing intrusion detection systems based on signature detection and anomalies. In order to more effectively detect new attacks, a model of anomaly detection using the Hurst exponent vector and the multifractal spectrum is proposed. It is shown that a multifractal analysis shows a sensitivity to any deviation of network traffic properties resulting from anomalies. Proposed traffic analysis methods can be ideal for protecting critical data and maintaining the continuity of internet services, including the IoT.