Cyber defence using attack graphs prediction and visualisation. (7th February 2023)

• Record Type: Journal Article
• Authors: Mishra, Shailendra
• Is Part Of: International journal of communication networks and distributed systems. Volume 29:Number 3(2023)
• Page Start: 268
• Page End: 289
• Publication Date: 2023-02-07
• Subjects: IDS network security -- attack graph -- adjacency matrix -- intrusion detection system -- machine learning -- cyber defence; Computer networks -- Periodicals; Telecommunication systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-3916

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.