用类脑神经网络实现持续学习的网络安全检测，抗遗忘且省电。
Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning
- 分两阶段：静态网络初筛，动态网络精分类，模拟生物适应机制。
- 在UNSW-NB15上达85.3%准确率，持续学习中遗忘少。
- 适合需要长期更新、低功耗部署的智能安防系统。
受大脑层级处理与能效启发，本文提出一种脉冲神经网络（SNN）架构，用于终身网络入侵检测系统（NIDS）。该系统首先使用高效静态SNN识别潜在入侵，随后激活自适应动态SNN以分类具体攻击类型。模仿生物适应性，动态分类器采用受GWR启发的结构可塑性与新型自适应尖峰时序依赖可塑性（Ad-STDP）学习规则。这些生物合理机制使网络能在保留已有知识的同时增量学习新威胁。在持续学习设置下，于UNSW-NB15基准测试中表现出强适应性、显著减少灾难性遗忘，整体准确率达85.3%。此外，基于Intel Lava框架的仿真显示高运行稀疏性，凸显其在类脑硬件上低功耗部署的潜力。
原文摘要 · Abstract (English)
Inspired by the brain's hierarchical processing and energy efficiency, this paper presents a Spiking Neural Network (SNN) architecture for lifelong Network Intrusion Detection System (NIDS). The proposed system first employs an efficient static SNN to identify potential intrusions, which then activates an adaptive dynamic SNN responsible for classifying the specific attack type. Mimicking biological adaptation, the dynamic classifier utilizes Grow When Required (GWR)-inspired structural plasticity and a novel Adaptive Spike-Timing-Dependent Plasticity (Ad-STDP) learning rule. These bio-plausible mechanisms enable the network to learn new threats incrementally while preserving existing knowledge. Tested on the UNSW-NB15 benchmark in a continual learning setting, the architecture demonstrates robust adaptation, reduced catastrophic forgetting, and achieves $85.3$\% overall accuracy. Furthermore, simulations using the Intel Lava framework confirm high operational sparsity, highlighting the potential for low-power deployment on neuromorphic hardware.
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