From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Nikolaos Polatidis, Elias Pimenidis, Michail Pavlidis, Spyridon Papastergiou, Haralambos Mouratidis

Research output: Contribution to journalArticleResearchpeer-review

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 in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.
Original languageEnglish
JournalEvolving Systems
DOIs
Publication statusPublished - 22 May 2018

Fingerprint

Recommender systems
Risk management
Supply chains
Information systems

Bibliographical note

This is a post-peer-review, pre-copyedit version of an article published in Evolving Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12530-018-9234-z

Keywords

  • Recommender systems
  • Cyber security
  • Attack graph generation
  • Attack prediction
  • Risk management

Cite this

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title = "From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks",
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 in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.",
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From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks. / Polatidis, Nikolaos; Pimenidis, Elias; Pavlidis, Michail; Papastergiou, Spyridon; Mouratidis, Haralambos.

In: Evolving Systems, 22.05.2018.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

AU - Polatidis, Nikolaos

AU - Pimenidis, Elias

AU - Pavlidis, Michail

AU - Papastergiou, Spyridon

AU - Mouratidis, Haralambos

N1 - This is a post-peer-review, pre-copyedit version of an article published in Evolving Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12530-018-9234-z

PY - 2018/5/22

Y1 - 2018/5/22

N2 - 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 in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.

AB - 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 in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.

KW - Recommender systems

KW - Cyber security

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KW - Attack prediction

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M3 - Article

JO - Evolving Systems

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