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Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning

Created by
  • Haebom

Author

Md Zesun Ahmed Mia, Malyaban Bal, Sen Lu, George M. Nishibuchi, Suhas Chelian, Srini Vasan, Abhronil Sengupta

Outline

Inspired by the brain's hierarchical processing and energy efficiency, this paper presents a spiking neural network (SNN) architecture for a lifelong network intrusion detection system (NIDS). The proposed system first uses an efficient static SNN to identify potential intrusions, then activates an adaptive dynamic SNN to classify specific attack types. Mimicking biological adaptation, the dynamic classifier leverages structural plasticity inspired by Grow When Required (GWR) and a novel adaptive spike-time-dependent plasticity (Ad-STDP) learning rule. These biologically plausible mechanisms enable the network to incrementally learn about new threats while retaining prior knowledge. Testing using the UNSW-NB15 benchmark in a continuous learning environment demonstrates robust adaptability, reduced fatal forgetting, and an overall accuracy of 85.3%. Furthermore, simulations using the Intel Lava framework demonstrate high operational sparsity, highlighting the potential for low-power deployment on neuromorphic hardware.

Takeaways, Limitations

Takeaways:
We present an efficient SNN-based NIDS architecture that mimics the brain's hierarchical processing and energy efficiency.
Achieving continuous learning and low forgetting rates through biologically plausible learning mechanisms based on GWR and Ad-STDP.
Achieved high accuracy (85.3%) on the UNSW-NB15 dataset.
Presenting the possibility of low-power implementation on neuromorphic hardware
Limitations:
Only performance evaluation is performed on a specific dataset (UNSW-NB15).
Lack of performance verification in real-world environments
Lack of comparative analysis with other NIDS systems
Further research is needed on the generalizability and limitations of the Ad-STDP learning rule.
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