# Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning

arXiv: https://arxiv.org/abs/2508.04610 · HTML: https://mlsanity.com/paper/2508.04610/

## 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 (most cited first, 6 of 6)

- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018)
- [ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks](https://mlsanity.com/paper/1703.07655/) (2017)
- [Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction](https://mlsanity.com/paper/2401.12262/) (2024)
- [Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks](https://mlsanity.com/paper/1902.03187/) (2019)

## Built on by (most cited first, 2 of 2)

- [Energy-Aware Spike Budgeting for Continual Learning in Spiking Neural Networks for Neuromorphic Vision](https://mlsanity.com/paper/2602.12236/) (2026)
- [Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks](https://mlsanity.com/paper/2609.09564/) (2026)

## 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.