Kevin Kwiat

Walid Saad, Anibal Sanjab, Yunpeng Wang et al.

Outsourcing integrated circuit (IC) manufacturing to offshore foundries has grown exponentially in recent years. Given the critical role of ICs in the control and operation of vehicular systems and other modern engineering designs, such offshore outsourcing has led to serious security threats due to the potential of insertion of hardware trojans - malicious designs that, when activated, can lead to highly detrimental consequences. In this paper, a novel game-theoretic framework is proposed to analyze the interactions between a hardware manufacturer, acting as attacker, and an IC testing facility, acting as defender. The problem is formulated as a noncooperative game in which the attacker must decide on the type of trojan that it inserts while taking into account the detection penalty as well as the damage caused by the trojan. Meanwhile, the resource-constrained defender must decide on the best testing strategy that allows optimizing its overall utility which accounts for both damages and the fines. The proposed game is based on the robust behavioral framework of prospect theory (PT) which allows capturing the potential uncertainty, risk, and irrational behavior in the decision making of both the attacker and defender. For both, the standard rational expected utility (EUT) case and the PT case, a novel algorithm based on fictitious play is proposed and shown to converge to a mixed-strategy Nash equilibrium. For an illustrative case study, thorough analytical results are derived for both EUT and PT to study the properties of the reached equilibrium as well as the impact of key system parameters such as the defender-set fine. Simulation results assess the performance of the proposed framework under both EUT and PT and show that the use of PT will provide invaluable insights on the outcomes of the proposed hardware trojan game, in particular, and system security, in general.

Omar Al-Ibrahim, Aziz Mohaisen, Charles Kamhoua et al.

Threat intelligence sharing has become a growing concept, whereby entities can exchange patterns of threats with each other, in the form of indicators, to a community of trust for threat analysis and incident response. However, sharing threat-related information have posed various risks to an organization that pertains to its security, privacy, and competitiveness. Given the coinciding benefits and risks of threat information sharing, some entities have adopted an elusive behavior of "free-riding" so that they can acquire the benefits of sharing without contributing much to the community. So far, understanding the effectiveness of sharing has been viewed from the perspective of the amount of information exchanged as opposed to its quality. In this paper, we introduce the notion of quality of indicators (\qoi) for the assessment of the level of contribution by participants in information sharing for threat intelligence. We exemplify this notion through various metrics, including correctness, relevance, utility, and uniqueness of indicators. In order to realize the notion of \qoi, we conducted an empirical study and taken a benchmark approach to define quality metrics, then we obtained a reference dataset and utilized tools from the machine learning literature for quality assessment. We compared these results against a model that only considers the volume of information as a metric for contribution, and unveiled various interesting observations, including the ability to spot low quality contributions that are synonym to free riding in threat information sharing.

Aziz Mohaisen, Omar Al-Ibrahim, Charles Kamhoua et al.

In the past decade, the information security and threat landscape has grown significantly making it difficult for a single defender to defend against all attacks at the same time. This called for introduc- ing information sharing, a paradigm in which threat indicators are shared in a community of trust to facilitate defenses. Standards for representation, exchange, and consumption of indicators are pro- posed in the literature, although various issues are undermined. In this paper, we rethink information sharing for actionable intelli- gence, by highlighting various issues that deserve further explo- ration. We argue that information sharing can benefit from well- defined use models, threat models, well-understood risk by mea- surement and robust scoring, well-understood and preserved pri- vacy and quality of indicators and robust mechanism to avoid free riding behavior of selfish agent. We call for using the differential nature of data and community structures for optimizing sharing.