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Entropy-based Network Traffic Anomaly Classification Method Resilient to Deception

Juma Ibrahim¹ and Slavko Gajin²

1. University of Belgrade – School of Electrical Engineering
   Bul. kralja Aleksandra 73, 11000 Belgrade, Serbia
   jumaibrahim04@yahoo.com
2. University of Belgrade – School of Electrical Engineering
   Bul. kralja Aleksandra 73, 11000 Belgrade, Serbia
   slavko.gajin@rcub.bg.ac.rs

Abstract

Entropy-based network traffic anomaly detection techniques are attractive due to their simplicity and applicability in a real-time network environment. Even though flow data provide only a basic set of information about network communications, they are suitable for efficient entropy-based anomaly detection techniques. However, a recent work reported a serious weakness of the general entropy-based anomaly detection related to its susceptibility to deception by adding spoofed data that camouflage the anomaly. Moreover, techniques for further classification of the anomalies mostly rely on machine learning, which involves additional complexity. We address these issues by providing two novel approaches. Firstly, we propose an efficient protection mechanism against entropy deception, which is based on the analysis of changes in different entropy types, namely Shannon, Rényi, and Tsallis entropies, and monitoring the number of distinct elements in a feature distribution as a new detection metric. The proposed approach makes the entropy techniques more reliable. Secondly, we have extended the existing entropy-based anomaly detection approach with the anomaly classification method. Based on a multivariate analysis of the entropy changes of multiple features as well as aggregation by complex feature combinations, entropy-based anomaly classification rules were proposed and successfully verified through experiments. Experimental results are provided to validate the feasibility of the proposed approach for practical implementation of efficient anomaly detection and classification method in the general real-life network environment.

Key words

anomaly classification, anomaly detection, entropy, entropy deception, network behaviour analysis

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS201229045I

Publication information

Volume 19, Issue 1 (January 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Ibrahim, J., Gajin, S.: Entropy-based Network Traffic Anomaly Classification Method Resilient to Deception. Computer Science and Information Systems, Vol. 19, No. 1, 87-116. (2022), https://doi.org/10.2298/CSIS201229045I