Hardware Trojan detection for lightweight ciphers implemented on field‐programmable gate arrays using the replay algorithm

Abstract

Summary Hardware Trojans (HTs) are malicious and intentional modification of the hardware design, embedded by an adversary to leak sensitive data, modify functionality, or cause malfunction. Lightweight ciphers are designed for resource‐constrained devices (RCDs) to balance resources and security, but often targeted by HTs. This work aims to create a trusted lightweight cipher design and detect complex sequential HTs using a runtime monitoring method with minimal resource overhead. The novelty of the approach is that it combines full or partial temporal redundancy, resource minimization, and runtime monitoring. The algorithm employs temporal redundancy, by executing a first run of the cipher, and then replays the cipher fully (ReplayN method) or partially (ReplayN/2 method). A mismatch indicates HT activity, and a third replay is executed to determine the correct results. The ReplayN/2 algorithm further optimizes time and energy by limiting replay to the second half of the cipher rounds. The proposed method is compared with other existing methods including triple modular redundancy (TMR), adapted triple modular redundancy (ATMR), and reverse‐function redundancy (RFR). All methods were implemented in field‐programmable gate array (FPGA) technology. Implementation performance metrics were measured and compared, including resource utilization (i.e., logical elements [LEs]), power, energy (E), and speed (i.e., Fmax). The results show that the proposed algorithm outperforms existing methods in most of the metrics. In terms of speed and area, the replay algorithm is faster by 24% and reduces total LEs by 42% when compared with existing methods. Regarding power, replay algorithm reduces power by 45% when compared with TMR and ATMR. With respect to energy, replay methods reduce energy by an average of 6% when compared with TMR and ATMR. The ReplayN/2 reduces energy by an average 15% when compared with TMR and ATMR. Based on the normalE×LE (i.e., energy × area) metric, ReplayN/2 is the top performing method as it balances area and energy metrics. Using normalE×LEnormalFmax metric, the replay methods are the best performing methods as they best balance area, energy, and speed. Also, based on ThroughputLE2 metric, the replay methods are the top performing methods.