Think Smart, Play Dumb: Analyzing Deception in Hardware Trojan Detection Using Game Theory

Abstract

In recent years, integrated circuits (ICs) have become<br>significant for various industries and their security has<br>been given greater priority, specifically in the supply chain.<br>Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multilevel game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zerosum, repeated game using prospect theory (PT) that captures different players’ rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender<br><div>learns about the attacker’s tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by “playing dumb” in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker’s view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected.</div><div><br></div><div>This paper has been accepted for publication in <b>IEEE Cyber Science Conference 2020</b><br></div>

Finding the Sweet Spot for Data Anonymization: A Mechanism Design Perspective

“…Other works in literature have studied the privacy, under possible attacks, by analyzing the interactions between the sharing organizations and the attacker using game theory [24]. Game theory is a powerful mathematical framework that enables to study the interactions between different decision makers and that is widely used in many security domains [25], [26], [27]. Similarly, game theory has been recently used to preserve the privacy of shared data [28], [29], [30].…”

“…where 1 is the indicator function which equals 1 in case of unsuccessful attack and equals 0 if the attack was successful. Following (25), the coefficient of trust will equal half of the value in the previous step if an attack was successful. On the other hand, if an attack was blocked by the common platform, the coefficient of trust will be the average of the previous value and 1.…”

“…This insures that the value of the coefficient remains between 0 and 1, when the initial value is less than 1. Note that, the design of the trust function in (25) will punish the successful attack more than it rewards blocking the attacks. Note that, this design is different from other trust coefficients proposed in literature, e.g., [46] which treat positive and negative updates equally.…”