Biblio
A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a mission team, has been widely used for emergent scenarios such as criminal tracking and victim assistance. These scenarios are related to human safety and require a robot team to quickly transit from the current undergoing task into the new emergent task. This sudden mission change brings difficulty in robot motion adjustment and increases the risk of performance degradation of the swarm. Trust in human-human collaboration reflects a general expectation of the collaboration; based on the trust humans mutually adjust their behaviors for better teamwork. Inspired by this, in this research, a trust-aware reflective control (Trust-R), was developed for a robot swarm to understand the collaborative mission and calibrate its motions accordingly for better emergency response. Typical emergent tasks “transit between area inspection tasks”, “response to emergent target - car accident” in social security with eight fault-related situations were designed to simulate robot deployments. A human user study with 50 volunteers was conducted to model trust and assess swarm performance. Trust-R's effectiveness in supporting a robot team for emergency response was validated by improved task performance and increased trust scores.
The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods.
The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or \$100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.
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.