RDAMS: An Efficient Run-Time Approach for Memory Fault and Hardware Trojans Detection

Ensuring the security of IoT devices and chips at runtime has become an urgent task as they have been widely used in human life. Embedded memories are vital components of SoC (System on Chip) in these devices. If they are attacked or incur faults at runtime, it will bring huge losses. In this paper, we propose a run-time detection architecture for memory security (RDAMS) to detect memory threats (fault and Hardware Trojans attack). The architecture consists of a Security Detection Core (SDC) that controls and enforces the detection procedure as a "security brain", and a memory wrapper (MEM_wrapper) which interacts with memory to assist the detection. We also design a low latency response mechanism to solve the SoC performance degradation caused by run-time detection. A block-based multi-granularity detection approach is proposed to render the design flexible and reduce the cost in implementation using the FPGA's dynamic partial reconfigurable (DPR) technology, which enables online detection mode reconfiguration according to the requirements. Experimental results show that RDAMS can correctly detect and identify 10 modeled memory faults and two types of Hardware Trojans (HTs) attacks without leading a great performance degradation to the system.