基于SVM-RFE和粒子群优化算法的恶意域名检测模型

域名解析作为网络建立连接的第一个步骤，对恶意域名进行快速识别是阻断异常网络行为的有效措施。本研究利用机器学习和随机搜索算法，提出了一种基于SVM-RFE和粒子群优化算法的恶意域名检测模型。分析域名字符特征、解析特征和注册特征，使用SVM-RFE算法进行特征权重排序，通过优化的粒子群算法确定最佳SVM参数和特征选择。实验证明该检测模型具有较好的效率和准确度。

A SVM-RFE and particle swarm optimization based detection model for malicious domain names

Domain name resolution is the first step of network communication. Fast identification of malicious domain name is an effective measure to block malicious network behavior. This study proposes a domain name detection model based on the SVM-RFE algorithm and particle swarm optimization method using machine learning and stochastic search algorithms. The character characteristics, parsing features and registration characteristics of the domain name are analyzed. The features are sorted by the SVM-RFE algorithm. The parameters and features of support vector machine are automatically determined by the particle swarm optimization algorithm. The experimental results show that the monitoring model has higher detection efficiency and better prediction accuracy.