基于SSA-SVM的网络入侵检测研究

互联网快速发展使得网络空间越来越复杂，网络入侵导致网络安全问题备受关注。为提升网络入侵的检测效率和精度，构建了基于支持向量机的网络入侵检测模型。支持向量机模型的惩罚系数和核函数参数直接影响入侵模型的检测精度，采用麻雀搜索算法对惩罚系数和核函数参数进行优化，提出了基于麻雀搜索算法和支持向量机的网络入侵检测模型。将提出的网络入侵检测模型应用于实际的网络入侵检测中，并与PSO-SVM和SVM模型进行对比。结果表明，所提出的网络入侵检测模型能够有效降低网络入侵的误报率，这对确保网络安全具有一定的现实意义。

Study on Network Intrusion Detection Based on SSA-SVM

The rapid development of the Internet has made the cyberspace more and more complex, and network intrusion has led to network security issues. In order to improve the efficiency and accuracy of network intrusion detection, a network intrusion detection model based on support vector machine was constructed. The penalty coefficient and kernel function parameters of the support vector machine model directly affect the detection accuracy of the intrusion model. Sparrow search algorithm is used to optimize the penalty coefficient and kernel function parameters. A network intrusion detection model based on sparrow search algorithm and support vector machine is proposed. The proposed network intrusion detection model is applied to the actual network intrusion detection, and compared with PSO-SVM and SVM models. The results show that the proposed network intrusion detection model can effectively reduce the false alarm rate of network intrusion, which is of practical significance to ensure network security.