DeMiST: Detection and Mitigation of Stealthy Analog Hardware Trojans

The global semiconductor supply chain involves design and fabrication at various locations, which leads to multiple security vulnerabilities, e.g., Hardware Trojan (HT) insertion. Although most HTs target digital circuits, HTs can be inserted in analog circuits. Therefore, several techniques have been developed for HT insertions in analog circuits. Capacitance-based Analog Hardware Trojan (AHT) is one of the stealthiest HT that can bypass most existing HT detection techniques because it uses negligible charge accumulation in the capacitor to generate stealthy triggers. To address the charge sharing and accumulation issues, we propose a novel way to detect such capacitance-based AHT in this paper. Secondly, we critically analyzed existing AHTs to highlight their respective limitations. We proposed a stealthier capacitor-based AHT (fortified AHT) that can bypass our novel AHT detection technique by addressing these limitations. Finally, by critically analyzing the proposed fortified AHT and existing AHTs, we developed a robust two-phase framework (DeMiST) in which a synchronous system can mitigate the effects of capacitance-based stealthy AHTs by turning off the triggering capability of AHT. In the first phase, we demonstrate how the synchronous system can avoid the AHT during run-time by controlling the supply voltage of the intermediate combinational circuits. In the second phase, we proposed a supply voltage duty cycle-based validation technique to detect capacitance-based AHTs. Furthermore, DeMiST amplified the switching activity for charge accumulation to such a degree that it can be easily detectable using existing switching activity-based HT detection techniques.Comment: Accepted at ACM Hardware and Architectural Support for Security and Privacy (HASP) 202

Making industrial robots smarter with adaptive reasoning and autonomous thinking for real-time tasks in dynamic environments: a case study.

In order to extend the abilities of current robots in industrial applications towards more autonomous and flexible manufacturing, this work presents an integrated system comprising real-time sensing, path-planning and control of industrial robots to provide them with adaptive reasoning, autonomous thinking and environment interaction under dynamic and challenging conditions. The developed system consists of an intelligent motion planner for a 6 degrees-of-freedom robotic manipulator, which performs pick-and-place tasks according to an optimized path computed in real-time while avoiding a moving obstacle in the workspace. This moving obstacle is tracked by a sensing strategy based on machine vision, working on the HSV space for color detection in order to deal with changing conditions including non-uniform background, lighting reflections and shadows projection. The proposed machine vision is implemented by an off-board scheme with two low-cost cameras, where the second camera is aimed at solving the problem of vision obstruction when the robot invades the field of view of the main sensor. Real-time performance of the overall system has been experimentally tested, using a KUKA KR90 R3100 robot

Making Industrial Robots Smarter with Adaptive Reasoning and Autonomous Thinking for Real-Time Tasks in Dynamic Environments: A Case Study

In order to extend the abilities of current robots in industrial applications towards more autonomous and flexible manufacturing, this work presents an integrated system comprising real-time sensing, path-planning and control of industrial robots to provide them with adaptive reasoning, autonomous thinking and environment interaction under dynamic and challenging conditions. The developed system consists of an intelligent motion planner for a 6 degrees-of-freedom robotic manipulator, which performs pick-and-place tasks according to an optimized path computed in real-time while avoiding a moving obstacle in the workspace. This moving obstacle is tracked by a sensing strategy based on machine vision, working on the HSV space for color detection in order to deal with changing conditions including non-uniform background, lighting reflections and shadows projection. The proposed machine vision is implemented by an off-board scheme with two low-cost cameras, where the second camera is aimed at solving the problem of vision obstruction when the robot invades the field of view of the main sensor. Real-time performance of the overall system has been experimentally tested, using a KUKA KR90 R3100 robot