HETCLNN: A Lightweight Intrusion Detection Network with Class-Aware Self-Knowledge Distillation

Abstract

With the proliferation of edge computing, building efficient NIDS faces challenges related to limited computational resources and class imbalance. To address the issues of high overhead and poor detection of rare attacks in existing models, this paper proposes a lightweight intrusion detection model based on Class-Aware Self-Knowledge Distillation (CASKD). Architecturally, we design a lightweight network utilizing Heterogeneous Convolution (HetConv)-based residual and inverted residual structures. For training, a temperature-based CASKD method is introduced to tackle extreme class imbalance. Experimental results on CIC-IDS2017 and Bot-IoT datasets demonstrate classification accuracies exceeding 99\%. The proposed method significantly reduces computational overhead while improving detection precision for rare attacks, achieving an optimal balance between model compactness and performance.