Security For System-On-Chip (SoC) Using Neural Networks
This addresses security issues for SoC designers and users in IoT and embedded systems, but it appears incremental as it focuses on comparing existing methods rather than introducing new solutions.
The paper tackles security threats in System-on-Chip (SoC) designs, such as hardware trojans and denial-of-service attacks, by comparing and describing how neural networks like Spiking Neural Networks can be used to prevent these attacks, though no concrete results or numbers are provided.
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.