Recommender systems meeting security: From product recommendation to cyber-attack prediction

Pavlidis, Michalis; Polatidis, Nikolaos; Pimenidis, Elias; Mouratidis, Haralambos

Abstract

© Springer International Publishing AG 2017. Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks. The proposed method has been experimentally evaluated and it is shown that it is both practical and effective.

Citation

Pavlidis, M., Polatidis, N., Pimenidis, E., & Mouratidis, H. (2017). Recommender systems meeting security: From product recommendation to cyber-attack prediction. Communications in Computer and Information Science, 744, 508-519. https://doi.org/10.1007/978-3-319-65172-9_43

• Journal Article Type: Conference Paper
• Publication Date: Jan 1, 2017
• Deposit Date: Sep 18, 2017
• Journal: Communications in Computer and Information Science
• Print ISSN: 1865-0929
• Publisher: Springer Verlag (Germany)
• Peer Reviewed: Peer Reviewed
• Volume: 744
• Pages: 508-519
• DOI: https://doi.org/10.1007/978-3-319-65172-9_43
• Keywords: recommender systems, cyber security, attack graph, exploit, vulnerability, attack prediction, classification
• Public URL: https://uwe-repository.worktribe.com/output/882753
• Publisher URL: http://www.springer.com/gb/book/9783319651712