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On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection
Announce Type: new Abstract: Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks (SNNs) are therefore a natural candidate, but their design space, spanning the choice of neuron model and spike encoding scheme, remains poorly characterized for intrusion...
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector
arXiv:2410.22967v5 Announce Type: replace Abstract: The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptive unsupervised Network...