Intrusion detection method for IoT in heterogeneous environment - LIU Jing; MU Zelin; LAI Yingxu (Tongxin xuebao)
In order to address the issue of inadequate training efficiency and subpar model performance encountered by Internet of things (IoT) devices when dealing with resource constraints and non-independent and identically distributed (Non-IID) data, a novel personalized pruning federated learning frame work for IoT intrusion detection was put forth. Initially, a channel importance scoring-based structured pruning strategy was proposed, facilitating the generation of sub-models to be disseminated to resource-limited clients, thereby harmonizing model accuracy and complexity. Subsequently, an innovative heterogeneous model aggregation algorithm was introduced, utilizing similarity-weighted coefficients for channel averaging, thereby effectively mitigating the adverse effects of Non-IID data during the model aggregation process. Ultimately, experimental results derived from the network intrusion dataset BoT-IoT substantiate that, relative to existing methods, the proposed method notably curtails the time expenditure of resource-constrained clients, and improves processing speed by 20.82%, while enhancing the accuracy of intrusion detection by 0.86% in Non-IID conditions.