混合GNN-LSTM防御具有差异隐私和安全的多方计算,用于边缘优化的神经形态自主系统
1Computer Science Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
Scientific reports
|December 16, 2025
概括
本研究介绍了一种用于自动驾驶汽车中神经形态计算的新型安全解决方案,增强对网络攻击的感知系统,同时保持边缘部署的隐私和效率.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 网络安全 网络安全
背景情况:
- 神经形态计算,利用尖端神经网络 (SNN) 和事件摄像头,为自动驾驶汽车 (AV) 提供节能感知.
- 这些系统易受对抗性攻击,故障注入和数据中毒的影响,构成重大安全风险.
- 对于资源有限的边缘环境,现有的防御通常是不够的.
研究的目的:
- 为神经形态自主系统开发强大高效的安全解决方案.
- 解决这些系统对各种对抗性威胁的易感性.
- 确保下一代AV的隐私意识和资源效率高的安全性.
主要方法:
- 提出了一种混合图形神经网络-长期短期记忆 (GNN-LSTM) 模型用于攻击检测.
- 集成差分隐私 (DP) 和安全多方计算 (SMPC) 保护隐私和减少威胁.
- 应用量子化和修剪技术来优化边缘部署的框架.
主要成果:
- 在KITTI多式联运实验中获得了94.3%的准确性,使攻击成功率降低了30%.
- 在神经形态N-Caltech101实验中达到92.4%的准确性,攻击成功率下降了27%.
- 证明了拟议解决方案对训练有素的对抗性攻击的有效性.
结论:
- 开发的安全解决方案为神经形态的AVS提供了实质性的,对隐私有意识的,资源高效的保护.
- 混合GNN-LSTM模型与DP和SMPC相结合,有效地减轻了对抗性威胁.
- 优化的框架确保了在自主系统中边缘部署的可行性.
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