Class-Aware Neural Networks for Efficient Intrusion Detection on Edge Devices

The exponential growth of IoT and edge devices has led to their widespread use across various applications. However, the security of these devices remains a significant concern due to their vulnerability to a broad spectrum of cyber-attacks. Network Intrusion Detection Systems (NIDS) are crucial for identifying and mitigating such threats. Traditional NIDS approaches, while effective, struggle to detect sophisticated modern attacks and often require substantial computational power and memory, which may not be feasible for edge devices. Machine learning and neural network-based methods have demonstrated promising improvements in NIDS detection accuracy. Yet, their deployment on resource-constrained edge devices presents a challenge. This has led to the development of Dynamic Neural Networks, an approach that allows models to adapt according to the input, making them more efficient and lightweight. However, these networks are class-agnostic, rendering them unsuitable for handling cases with uneven classification priorities. In this paper, we introduce ClassyNet, a platform designed for efficient, class-aware NIDS on edge devices. ClassyNet leverages class-specific feature extraction and a class-specific neural network architecture to enhance intrusion detection efficiency. Experimental results indicate that our proposed approach matches the detection accuracy of traditional machine learning and neural network-based methods while significantly improving resource efficiency.