Visible to the public Biblio

Filters: Author is Mitchell, R.  [Clear All Filters]
2017-12-28
Mitchell, R., Walkup, E..  2017.  Further refinements to the foundations of cyber zone defense. MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM). :823–828.

Sophisticated cyber attacks by state-sponsored and criminal actors continue to plague government and industrial infrastructure. Intuitively, partitioning cyber systems into survivable, intrusion tolerant compartments is a good idea. This prevents witting and unwitting insiders from moving laterally and reaching back to their command and control (C2) servers. However, there is a lack of artifacts that can predict the effectiveness of this approach in a realistic setting. We extend earlier work by relaxing simplifying assumptions and providing a new attacker-facing metric. In this article, we propose new closed-form mathematical models and a discrete time simulation to predict three critical statistics: probability of compromise, probability of external host compromise and probability of reachback. The results of our new artifacts agree with one another and with previous work, which suggests they are internally valid and a viable method to evaluate the effectiveness of cyber zone defense.

2015-04-30
Mitchell, R., Ing-Ray Chen.  2014.  Adaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications. Systems, Man, and Cybernetics: Systems, IEEE Transactions on. 44:593-604.


In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.