mlsanityData to 14 Oct 2026

Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning

arXiv 2508.04610 ↗First posted 6 Aug 2025cs.LG cs.AI cs.ETcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 6 Aug 2025.

Builds on

6 of 14 references.

References · 6

  1. 2017Measuring Catastrophic Forgetting in Neural Networkscited by 360
  2. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  3. 2018BindsNET: A machine learning-oriented spiking neural networks library in Pythoncited by 43
  4. 2017ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networkscited by 16
  5. 2024Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extractioncited by 16
  6. 2019Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networkscited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Energy-Aware Spike Budgeting for Continual Learning in Spiking Neural Networks for Neuromorphic Visioncited by 0
  2. 2026Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attackscited by 0