Enhanced PATRON: Fault Injection and Power-aware FSM Encoding Through Linear Programming

Abstract

Since finite state machines (FSMs) regulate the control flow in circuits, a computing system’s security might be breached by attacking the FSM. Physical attacks are especially worrisome because they can bypass software countermeasures. For example, an attacker can gain illegal access to the sensitive states of an FSM through fault injection, leading to privilege escalation and/or information leakage. Laser fault injection (LFI) provides one of the most effective attack vectors by enabling adversaries to precisely overturn single flip-flops states. Although conventional error correction/detection methodologies have been employed to improve FSM resiliency, their substantial overhead makes them unattractive to circuit designers. In our prior work, a novel decision diagram-based FSM encoding scheme called PATRON was proposed to resist LFI according to attack parameters, e.g., number of simultaneous faults. Although PATRON bested traditional encodings keeping overhead minimum, it provided numerous candidates for FSM designs requiring exhaustive and manual effort to select one optimum candidate. In this article, we automatically select an optimum candidate by enhancing PATRON using linear programming (LP). First, we exploit the proportionality between dynamic power dissipation and switching activity in digital CMOS circuits. Thus, our LP objective minimizes the number of FSM bit switches per transition, for comparatively lower switching activity and hence total power consumption. Second, additional LP constraints along with incorporating the original PATRON rules, systematically enforce bidirectionality to at least two state elements per FSM transition. This bestows protection against different types of fault injection, which we capture with a new unidirectional metric. Enhanced PATRON (EP) achieves superior security at lower power consumption in average compared to PATRON, error-coding, and traditional FSM encoding on five popular benchmarks.