基于自适应STDP和脉冲图神经网络的神经形态图异常检测
Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
AI总结：
本文提出ASTDP-GAD框架，结合脉冲图神经网络与STDP学习，实现动态网络中的高效异常检测，通过自适应LIF动态、图注意力、超图记忆等创新方法提升检测精度与能效。
摘要：
在动态网络中进行异常检测对网络安全和工业监控至关重要，但现有方法在能耗、时间精度和适应性方面面临挑战。本文介绍了ASTDP-GAD，一种用于图异常检测的自适应脉冲时间动态可塑性框架，整合了脉冲图神经网络与STDP学习，以实现动态网络中的高效神经形态检测。我们的框架通过以下关键创新统一了脉冲神经计算、STDP学习和基于图的异常检测：具有自适应泄漏积分-微分（LIF）动态的时序脉冲图编码；基于LIF的图注意力机制与侧抑制；事件驱动的超图记忆与STDP启发的原型更新；基于脉冲不规则性的脉冲率对比池化；自适应STDP层捕捉因果时间关系；以及多尺度时间卷积与多因素异常融合。理论分析提供了严格保证：脉冲编码在模拟步骤中线性缩放的分辨率下保持输入信息；LIFGAT近似任何连续注意力函数；超图记忆收敛到最优原型；对比池化实现可证明的异常选择界；STDP学习稳定收敛；多因素融合产生校准的分数，具有高达5倍的方差减少。在九个动态和静态图数据集上的广泛实验表明，该方法在保持生物合理性和能效的同时，实现了优越的异常检测精度，适用于神经形态部署。
英文摘要：
Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper introduces ASTDP-GAD, a novel Adaptive Spiking Temporal Dynamics Plasticity framework for Graph Anomaly Detection that integrates spiking graph neural networks with STDP learning for energy-efficient neuromorphic detection in dynamic networks. Our framework unifies spiking neural computation, STDP learning, and graph-based anomaly detection through the following key innovations: temporal spike graph encoding with adaptive Leaky Integrate-and-Fire (LIF) dynamics; LIF-based graph attention with lateral inhibition; event-driven hypergraph memory with STDP-inspired prototype updates; spike rate contrast pooling based on spiking irregularity; adaptive STDP layers capturing causal temporal relationships; and multi-scale temporal convolution with multi-factor anomaly fusion. Theoretical analysis provides rigorous guarantees: spike encoding preserves input information with resolution scaling linearly in simulation steps and hidden dimension; LIFGAT approximates any continuous attention function; hypergraph memory converges to optimal prototypes; contrast pooling achieves provable anomaly selection bounds; STDP learning converges stably; and multi-factor fusion produces calibrated scores with up to $5\times$ variance reduction. Extensive experiments on nine datasets on both dynamic and static graphs demonstrate superior anomaly detection accuracy while maintaining biological plausibility and energy efficiency for neuromorphic deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
