S4oC: A Self-optimizing, Self-adapting Secure System-on-Chip Design Framework to Tackle Unknown Threats -- A Network Theoretic, Learning Approach
This addresses security challenges in hardware design for embedded systems, but it appears incremental as it builds on existing methods like neural networks and reinforcement learning.
The paper tackles the problem of securing system-on-chip architectures against unknown threats like hardware Trojans and side-channel attacks by proposing a self-optimizing, self-adapting framework (S4oC) that learns to reconfigure itself in real-time, resulting in a manycore system modeled as a four-layer graph with security-driven community detection and distributed reinforcement learning for task mapping.
We propose a framework for the design and optimization of a secure self-optimizing, self-adapting system-on-chip (S4oC) architecture. The goal is to minimize the impact of attacks such as hardware Trojan and side-channel, by making real-time adjustments. S4oC learns to reconfigure itself, subject to various security measures and attacks, some of which possibly unknown at design time. Furthermore, the data types and patterns of the target applications, environmental conditions, and sources of variations are incorporated. S4oC is a manycore system, modeled as a four-layer graph, representing the model of computation (MoCp), model of connection (MoCn), model of memory (MoM) and model of storage (MoS), with a large number of elements including heterogeneous reconfigurable processing elements in MoCp, and memory elements in the MoM layer. Security driven community detection, and neural networks are utilized for application task clustering, and distributed reinforcement learning (RL) for task mapping.