Lightweight Intrusion Detection System with GAN-Based Knowledge Distillation

In the rapidly changing realm of network security, creating efficient and flexible Intrusion Detection Systems (IDS) is crucial to combat the escalating complexity of network threats. Our study presents an innovative methodology that combines Generative Adversarial Networks (GANs) with knowledge distillation strategies to amplify the effectiveness of IDS, particularly in settings limited by computational resources like the Internet of Things (IoT) and Industrial Internet of Things (1IoT) networks. The fundamental novelty of our suggested system resides in its utilisation of GANs to produce varied datasets, which are subsequently employed to train deep learning models customised for intrusion detection. This approach empowers the IDS to adjust to different network setups and guarantees thorough defence against a broad range of attacks, emphasising adversarial assaults. By utilising knowledge distillation, we simplify the development of compact models that uphold the detection capabilities of their more intricate counterparts, presenting an optimal solution for settings with limited resources. Merging adversarial training with knowledge distillation enhances the IDS's resilience against adversarial threats. Empirical findings validate the efficiency of our method, showcasing its capacity to sustain high accuracy rates while ensuring resource effectiveness and adaptability in intricate and resource-constrained environments. This study signifies a notable progression in network security, providing a versatile, effective, and resilient IDS solution capable of functioning under diverse network conditions without compromising performance.