Toward an integrated dynamic defense system for strategic detecting attacks in cloud networks using stochastic game

El Mehdi Kandoussi (), Mohamed Hanini (), Iman Mir () and Abdelkrim Haqiq ()
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El Mehdi Kandoussi: Hassan 1st University
Mohamed Hanini: Hassan 1st University
Iman Mir: University Abdelmalek Essaadi
Abdelkrim Haqiq: Hassan 1st University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2020, vol. 73, issue 3, No 5, 397-417

Abstract: Abstract In a complex network as a cloud computing environment, security is becoming increasingly based on deception techniques. To date, the static nature of cyber networks offers to adversaries good opportunities to systematically study the network environment, launch a cyber-attack effortlessly and wide-spread and finally defeat the target system. In order to resolve the limitations of the traditional security measures as intrusion prevention or detection systems, firewall, access list, etc., which did not change the attack surface and cannot avoid zero-days attacks, technics that provide dynamic defense, such virtual machine migration and honeypot should be deployed. Despite this, with a virtual machine migration technique, not all virtual machines’ migration between servers enhances security considerably. In this paper, we propose an integrated defense system combining virtual machine migration and honeypot. The effectiveness of the proposed system is discussed in terms of security policies. In addition, our proposed model determines the potential attack paths quantitatively then classifies them into two sub-sets: attack paths explored only and attack paths explored and exploited based on the black box intrusion steps. Thus, to model the interaction attacker–defender, the attack graph combined with the stochastic game theory is used. Finally, we carry out some numerical results to demonstrate the effectiveness of the proposed security game model.

Keywords: Honeypot; VM migration; Cloud computing; Stochastic game; Bayesian game; Attack graph (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11235-019-00616-1

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