Real-Time Network Traffic Anomaly Detection Using Spiking Neural Networks (SNNs) with Adaptive Learning

This paper presents a novel real-time intrusion detection framework that leverages Spiking Neural Networks (SNNs) for detecting anomalies and cyberattacks in network traffic. Inspired by the biological functioning of the brain, SNNs process information using discrete spikes over time, enabling efficient handling of spatiotemporal patterns in traffic data. The proposed approach dynamically adapts to new and evolving attack strategies through Spike-Timing-Dependent Plasticity (STDP), a biologically inspired learning mechanism that adjusts synaptic weights based on the precise timing of neuron activations. This adaptability allows the system to detect zero-day attacks without requiring frequent retraining, a key advantage over traditional machine learning and deep learning models.

The proposed system was evaluated using well-established cybersecurity datasets, NSL-KDD and CIC-IDS2017, covering a broad spectrum of attack types, including DDoS, brute force attacks, infiltration attempts, and port scanning. Comparative experiments demonstrate that the SNN-based detection system consistently outperforms traditional models, such as Random Forest, Support Vector Machines (SVM), and conventional deep learning architectures, in terms of detection accuracy, adaptability, and computational efficiency. The system achieves high detection accuracy while maintaining low false positive rates and significantly reducing detection time, making it highly suitable for real-time deployment in modern network environments.
This research highlights the potential of neuromorphic computing in the field of cybersecurity, offering a scalable, adaptive, and energy-efficient solution for intrusion detection in evolving network infrastructures.