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

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Polatidis, Nikolaos, Pimenidis, Elias, Pavlidis, Michalis and Mouratidis, Haralambos (2017) Recommender systems meeting security: from product recommendation to cyber-attack prediction In: International Conference on Engineering Applications of Neural Networks, Athens, Greece, 25-27 August, 2017.

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Abstract

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.

Item Type: Contribution to conference proceedings in the public domain ( Full Paper)
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-65172-9_43
Uncontrolled Keywords: Recommender systems; Cyber security; Attack graph; Exploit; Vulnerability; Attack prediction; Classification
Subjects: ?? G720 ??
G000 Computing and Mathematical Sciences > G700 Artificial Intelligence
G000 Computing and Mathematical Sciences > G400 Computing > G420 Networks and Communications
?? G590 ??
DOI (a stable link to the resource): 10.1007/978-3-319-65172-9_43
Depositing User: Converis
Date Deposited: 29 Sep 2017 03:01
Last Modified: 03 Nov 2017 09:34
URI: http://eprints.brighton.ac.uk/id/eprint/17443

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