Title: Cyber defence using attack graphs prediction and visualisation

Authors: Shailendra Mishra

Addresses: Department of Computer Engineering, Majmaah University, Majmaah – 11952, Saudi Arabia

Abstract: The use of the internet and other related technologies has increased dramatically in recent years. Since sensitive and critical data is readily available on these systems, this information can easily be accessed. Information leaks or attacks on networked devices are becoming more common every day. This research explores the visualisation of attack graphs in public cyberspace to predict exploit paths across networks. Vulnerability analysis reveals various aspects of the system that are exploited. By combining graph adjacency matrices cyberattack graphs are created. With the attack graph, grey areas and research points can be easily identified. Cybersecurity and network administration can be achieved by analysing M-steps. Moreover, machine learning algorithms such as SVM, RF, KNN, LR, and multilayer perceptron (MLP) are used to detect the attack and analyse the performance of the proposed system. In terms of accuracy, recall, precession, and F-score, RF and MLP were the best classifiers.

Keywords: IDS network security; attack graph; adjacency matrix; intrusion detection system; machine learning; cyber defence.

DOI: 10.1504/IJCNDS.2023.130566

International Journal of Communication Networks and Distributed Systems, 2023 Vol.29 No.3, pp.268 - 289

Received: 17 Feb 2022
Accepted: 17 Mar 2022
Published online: 28 Apr 2023 *

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