面向异构环境的物联网入侵检测方法OA北大核心CSTPCD

In order to address the issue of inadequate training efficiency and subpar model performance encountered by In-ternet 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.Ini-tially,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 pro-cess.Ultimately,experimental results derived from the network intrusion dataset BoT-IoT substantiate that,relative to ex-isting 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.