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Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network

Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis


Shancang Li
Senior Lecturer in Computer Forensics and Security

Theo Tryfonas

Gordon Russell

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Dr Panos Andriotis
Senior Lecturer in Computer Forensics and Security


© 2015 IEEE. Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.


Li, S., Tryfonas, T., Russell, G., & Andriotis, P. (2016). Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network. IEEE Transactions on Cybernetics, 46(8), 1749-1759.

Journal Article Type Article
Acceptance Date Feb 20, 2016
Publication Date Aug 1, 2016
Journal IEEE Transactions on Cybernetics
Print ISSN 2168-2267
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 46
Issue 8
Pages 1749-1759
Keywords mobile communication, security, Bayes methods, analytical models, risk management, hidden Markov models
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Additional Information Additional Information : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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