Security For System-On-Chip (SoC) Using Neural Networks

TL;DR

This paper discusses how neural networks, including Spiking Neural Networks and Runtime Neural Architecture, can enhance security in System-on-Chip devices against various malicious threats in IoT environments.

Contribution

It compares different neural network approaches for SoC security and highlights recent development trends in this field.

Findings

Neural networks can effectively detect and prevent security threats in SoCs.

Spiking Neural Networks and RTNA are promising for real-time security applications.

Development trends indicate increasing integration of neural networks in SoC security solutions.

Abstract

With the growth of embedded systems, VLSI design phases complexity and cost factors across the globe and has become outsourced. Modern computing ICs are now using system-on-chip for better on-chip processing and communication. In the era of Internet-of-Things (IoT), security has become one of the most crucial parts of a System-on-Chip (SoC). Malicious activities generate abnormal traffic patterns which affect the operation of the system and its performance which cannot be afforded in a computation hungry world. SoCs have a chance of functionality failure, leakage of information, even a denial of services (DoS), Hardware Trojan Horses and many more factors which are categorized as security threats. In this paper, we aim to compare and describe different types of malicious security threats and how neural networks can be used to prevent those attacks. Spiking Neural Networks (SNN), Runtime Neural Architecture (RTNA) are some of the neural networks which prevent SoCs from attacks. Finally, the development trends in SoC security are also highlighted.