@inproceedings{34ab463906124c5fb187d4c0fc947d2a,
title = "Recommender systems meeting security: from product recommendation to cyber-attack prediction",
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.",
keywords = "Recommender systems, Cyber security, Attack graph, Exploit, Vulnerability, Attack prediction, Classification",
author = "Nikolaos Polatidis and Elias Pimenidis and Michalis Pavlidis and Haralambos Mouratidis",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-65172-9_43; International Conference on Engineering Applications of Neural Networks ; Conference date: 02-08-2017",
year = "2017",
month = aug,
day = "2",
doi = "10.1007/978-3-319-65172-9_43",
language = "English",
isbn = "9783319651729",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "508--519",
booktitle = "International Conference on Engineering Applications of Neural Networks",
}
