Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices

Core Concepts

Spikewhisper introduces a novel temporal spike backdoor attack on Federated Neuromorphic Learning, enhancing stealthiness and effectiveness.

Abstract

Introduction: Introduces Federated Neuromorphic Learning (FedNL) and the threat of backdoor attacks. System Model: Discusses the threat model, attacker abilities, and attack objectives in FedNL. Spikewhisper Framework: Details the concept of Time Division Multiplexing and the design of Spikewhisper. Experiment: Evaluates Spikewhisper on N-MNIST and CIFAR10-DVS datasets, showing superior performance over temporally centralized attacks. Ablation Study: Explores the impact of trigger duration, size, and location on Spikewhisper's effectiveness. Conclusion & Future Work: Concludes the study and suggests the need for dedicated defense strategies in FedNL.

Stats

"Extensive experiments based on two different neuromorphic datasets demonstrate that the attack success rate of Spikewispher is higher than the temporally centralized attacks." "Training the GPT-3 model consumed about 190,000 kWh of electricity." "The accuracy of FedNL is 15% higher than that of DNNs on CIFAR10." "Spikewhisper achieves state-of-the-art attack effects against temporal centralized backdoor attacks."

Quotes

"Spikewhisper successfully injected a backdoor into the global SNN model, demonstrating its potential threat to federated neuromorphic learning systems." "The longer the duration of the trigger, the more likely the success of the attack in the federated neuromorphic learning scenario."

Key Insights Distilled From

Spikewhisper

by Hanqing Fu,G... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18607.pdf

Deeper Inquiries

How can the concept of Time Division Multiplexing be applied to enhance security in other domains

The concept of Time Division Multiplexing (TDM) can be applied to enhance security in various domains beyond federated neuromorphic learning. For instance, in the field of telecommunications, TDM is commonly used to optimize the utilization of communication channels by interleaving multiple signals within different time slots. This technique can be adapted to improve security in network communications by allowing for the secure transmission of multiple data streams over a shared channel. By segmenting the communication channel into distinct time slots for different data streams, TDM can help prevent data interference and unauthorized access, thereby enhancing the overall security of the network.

What are the potential ethical implications of using Spikewhisper in real-world applications

The use of Spikewhisper in real-world applications raises several ethical implications that need to be carefully considered. One major concern is the potential for malicious actors to exploit Spikewhisper for nefarious purposes, such as conducting covert backdoor attacks on sensitive systems or manipulating AI models for malicious intent. This could lead to serious consequences, including privacy breaches, data manipulation, and compromised system integrity. Additionally, the deployment of Spikewhisper in critical applications, such as healthcare or finance, could pose significant risks to individuals and organizations if the backdoor attacks are successful. Therefore, ethical considerations around transparency, accountability, and data security must be prioritized when utilizing Spikewhisper in real-world scenarios.

How can the findings of this study impact the development of future neuromorphic learning systems

The findings of this study can have a profound impact on the development of future neuromorphic learning systems. By uncovering the vulnerability of federated neuromorphic learning to temporal spike backdoor attacks like Spikewhisper, researchers and developers can now focus on enhancing the security measures of these systems. This study highlights the importance of incorporating robust defense mechanisms against backdoor attacks in neuromorphic learning models, paving the way for more secure and reliable AI applications. Moving forward, the insights gained from this research can inform the design and implementation of advanced security protocols and strategies to safeguard neuromorphic learning systems from potential threats and vulnerabilities.