Biblio

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.
 

In military operation or emergency response situations, very frequently a commander will need to assemble and dynamically manage Community of Interest (COI) mobile groups to achieve a critical mission assigned despite failure, disconnection or compromise of COI members. We combine the designs of COI hierarchical management for scalability and reconfigurability with COI dynamic trust management for survivability and intrusion tolerance to compose a scalable, reconfigurable, and survivable COI management protocol for managing COI mission-oriented mobile groups in heterogeneous mobile environments. A COI mobile group in this environment would consist of heterogeneous mobile entities such as communication-device-carried personnel/robots and aerial or ground vehicles operated by humans exhibiting not only quality of service (QoS) characters, e.g., competence and cooperativeness, but also social behaviors, e.g., connectivity, intimacy and honesty. A COI commander or a subtask leader must measure trust with both social and QoS cognition depending on mission task characteristics and/or trustee properties to ensure successful mission execution. In this paper, we present a dynamic hierarchical trust management protocol that can learn from past experiences and adapt to changing environment conditions, e.g., increasing misbehaving node population, evolving hostility and node density, etc. to enhance agility and maximize application performance. With trust-based misbehaving node detection as an application, we demonstrate how our proposed COI trust management protocol is resilient to node failure, disconnection and capture events, and can help maximize application performance in terms of minimizing false negatives and positives in the presence of mobile nodes exhibiting vastly distinct QoS and social behaviors.

In military operation or emergency response situations, very frequently a commander will need to assemble and dynamically manage Community of Interest (COI) mobile groups to achieve a critical mission assigned despite failure, disconnection or compromise of COI members. We combine the designs of COI hierarchical management for scalability and reconfigurability with COI dynamic trust management for survivability and intrusion tolerance to compose a scalable, reconfigurable, and survivable COI management protocol for managing COI mission-oriented mobile groups in heterogeneous mobile environments. A COI mobile group in this environment would consist of heterogeneous mobile entities such as communication-device-carried personnel/robots and aerial or ground vehicles operated by humans exhibiting not only quality of service (QoS) characters, e.g., competence and cooperativeness, but also social behaviors, e.g., connectivity, intimacy and honesty. A COI commander or a subtask leader must measure trust with both social and QoS cognition depending on mission task characteristics and/or trustee properties to ensure successful mission execution. In this paper, we present a dynamic hierarchical trust management protocol that can learn from past experiences and adapt to changing environment conditions, e.g., increasing misbehaving node population, evolving hostility and node density, etc. to enhance agility and maximize application performance. With trust-based misbehaving node detection as an application, we demonstrate how our proposed COI trust management protocol is resilient to node failure, disconnection and capture events, and can help maximize application performance in terms of minimizing false negatives and positives in the presence of mobile nodes exhibiting vastly distinct QoS and social behaviors.