基于ANFIS乌鸦搜索算法的网络入侵检测性能的优化

Performance Optimization of Network Intrusion Detection Based on ANFIS Crow Search Algorithm

入侵检测系统(IDS)用于检测网络或系统中的异常情况,对网络安全起着至关重要的作用.为降低误报率(FAR),提出了一种基于自适应神经模糊推理系统的乌鸦搜索优化算法(CSO-ANFIS).基于NSL-KDD数据集的入侵检测结果表明,所提模型检测率为95.80%,FAR为3.45%.

Intrusion Detection Systems(IDS)play a crucial role in ensuring network security by detecting abnormal conditions in networks or systems.To reduce the False Alarm Rate(FAR),a Crow Search Optimization-based Adaptive Neuro-Fuzzy Inference System(CSO-ANFIS)is proposed.The intrusion detection results based on the NSL-KDD dataset show that the proposed model achieved a detection rate of 95.80%and a FAR of 3.45%,which is better than other models.