EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks

Spiking neural networks (SNNs) are emerging as energy-efficient alternatives to traditional artificial neural networks (ANNs). Their event-driven information processing significantly reduces computational demands while maintaining competitive performance. However, as SNNs are increasingly deployed in edge devices, security concerns have emerged. While significant research efforts have been dedicated to addressing the security vulnerabilities stemming from malicious input, often referred to as adversarial examples, the security of SNN parameters remains relatively unexplored. This work introduces a novel attack methodology for SNNs known as Energy-Oriented SNN attack (EOS). EOS is designed to increase the energy consumption of SNNs through the malicious manipulation of binary bits within their memory systems (i.e., DRAM), where neuronal information is stored. The key insight of EOS lies in the observation that energy consumption in SNN implementations is intricately linked to spiking activity. The bit-flip operation, the well-known Row Hammer technique, is employed in EOS. It achieves this by identifying the most robust neurons in the SNN based on the spiking activity, particularly those related to the firing threshold, which is stored as binary bits in memory. EOS employs a combination of spiking activity analysis and a progressive search strategy to pinpoint the target neurons for bit-flip attacks. The primary objective is to incrementally increase the energy consumption of the SNN while ensuring that accuracy remains intact. With the implementation of EOS, successful attacks on SNNs can lead to an average of 43% energy increase with no drop in accuracy.