TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans
Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many security threats, like piracy of IC intellectual property or insertion of hardware Trojans, i.e., malicious circuit modifications. In this work, we proactively and systematically protect the physical layouts of ICs against post-design insertion of Trojans. Toward that end, we propose TroLLoc, a novel scheme for IC security closure that employs, for the first time, logic locking and layout hardening in unison. TroLLoc is fully integrated into a commercial-grade design flow, and TroLLoc is shown to be effective, efficient, and robust. Our work provides in-depth layout and security analysis considering the challenging benchmarks of the ISPD'22/23 contests for security closure. We show that TroLLoc successfully renders layouts resilient, with reasonable overheads, against (i) general prospects for Trojan insertion as in the ISPD'22 contest, (ii) actual Trojan insertion as in the ISPD'23 contest, and (iii) potential second-order attacks where adversaries would first (i.e., before Trojan insertion) try to bypass the locking defense, e.g., using advanced machine learning attacks. Finally, we release all our artifacts for independent verification [2].
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Security Closure of IC Layouts Against Hardware Trojans
Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many threats, like piracy of IC intellectual prop…
Security Hardening Using FABRIC: Implementing a Unified Compliance Aggregator for Linux Servers
This paper presents a unified framework for evaluating Linux security hardening on the FABRIC testbed through aggregation of heterogeneous security auditing tools. We deploy three Ubuntu 22.04 nodes configured at baselin…
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving
Recent research in adversarial machine learning has focused on visual perception in Autonomous Driving (AD) and has shown that printed adversarial patches can attack object detectors. However, it is important to note tha…
Autonomous DrivingMultiple Object TrackingObjectobject-detection+2Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach
Logic locking is a prominent technique to protect the integrity of hardware designs throughout the integrated circuit design and fabrication flow. However, in recent years, the security of locking schemes has been thorou…
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning
This paper addresses a significant gap in Autonomous Cyber Operations (ACO) literature: the absence of effective edge-blocking ACO strategies in dynamic, real-world networks. It specifically targets the cybersecurity vul…
BlockingDiversityreinforcement-learningReinforcement Learning (RL)