Risk assessment for mobile systems through a multilayered hierarchical Bayesian network.

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

Abstract

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

Citation

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. https://doi.org/10.1109/TCYB.2016.2537649

• Journal Article Type: Article
• Acceptance Date: Feb 20, 2016
• Online Publication Date: Apr 4, 2016
• Publication Date: 2016-08
• Deposit Date: May 2, 2016
• Publicly Available Date: Jul 12, 2017
• Journal: IEEE Transactions on Cybernetics
• Print ISSN: 2168-2267
• Electronic ISSN: 2168-2275
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 46
• Issue: 8
• Pages: 1749-1759
• DOI: https://doi.org/10.1109/TCYB.2016.2537649
• Keywords: Android malware; Bayesian risks graphs; mobile security;risk assessment
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/9987
• Publisher URL: http://dx.doi.org/10.1109/TCYB.2016.2537649