Work place: Department of Computer Science, University of Alabama, Tuscaloosa, AL 35478, USA
E-mail: atkison@cs.ua.edu
Website:
Research Interests: Computer systems and computational processes, Systems Architecture, Information Security, Intrusion Detection System, Network Security, Information Systems, Information Storage Systems, Detection Theory
Biography
Dr. Travis Atkison received a B.S. in Electrical Engineering and a B.S. in Computer Science in 1995 from the University of Alabama, a M.S. degree in Computer Science in 1997 from University of Alabama, and a Ph.D. in Computer Science in 2009 from Mississippi State University. He, currently, is an Assistant Professor of Computer Science and the Director of the Digital Forensics and Control System Security Lab (DCSL) at the University of Alabama. His major research avenues include transportation infrastructure security, control systems security, malicious application detection, and digital forensics.
By Shariful Haque Travis Atkison
DOI: https://doi.org/10.5815/ijcnis.2017.11.01, Pub. Date: 8 Nov. 2017
The advancement of modern day computing has led to an increase of threats and intrusions. As a result, advanced security measurements and threat analysis models are necessary to detect these threats and identify protective measures needed to secure a system. Attack graphs and attack trees are the most popular form of attack modeling today. While both of these approaches represent the possible attack steps followed by an attacker, attack trees are architecturally more rigorous than attack graphs and provide more insights regarding attack scenarios. The goal of this research is to identify the possible direction to construct attack trees from attack graphs analyzing a large volume of data, alerts or logs generated through different intrusion detection systems or network configurations. This literature summarizes the different approaches through an extensive survey of the relevant papers and identifies the current challenges, requirements and limitations of an efficient attack modeling approach with attack graphs and attack trees. A discussion of the current state of the art is presented in the later part of the paper, followed by the future direction of research.
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