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.

翻译：动态网络中的异常检测在从网络安全到工业监控等领域具有关键应用价值，然而现有方法在能效、时间精度和自适应性方面面临挑战。本文提出ASTDP-GAD——一种面向图异常检测的新型自适应脉冲时序动态塑性框架，该框架融合脉冲图神经网络与STDP学习机制，实现动态网络中具有高能效特性的神经形态异常检测。我们通过以下关键创新统一了脉冲神经计算、STDP学习与基于图的异常检测：具有自适应泄露整合发放（LIF）动力学的时序脉冲图编码；基于侧向抑制的LIF图注意力机制；受STDP启发的原型更新的事件驱动超图记忆；基于脉冲不规则性的脉冲率对比池化；捕获因果时序关系的自适应STDP层；以及多尺度时序卷积与多因子异常融合。理论分析提供了严格保证：脉冲编码在模拟步长和隐藏维度上以线性分辨率缩放保留输入信息；LIFGAT可逼近任意连续注意力函数；超图记忆收敛于最优原型；对比池化实现可证明的异常选择边界；STDP学习稳定收敛；多因子融合产生的校准分数方差降低高达$5\times$。在九个动态与静态图数据集上的大量实验表明，该方法在保持生物合理性与神经形态部署能效的同时，实现了优越的异常检测精度。