刘京龙

随着互联网发展与网络攻击增加，组织广泛采用入侵检测系统(IDS)，但仍面临高误报(FP)、高漏报(FN)及精度不足的挑战。引入机器学习分类器能有效解决上述问题。本研究基于NSL-KDD、UNSW-NB15和Phishing三个数据集，评估了J48、RF、K-NN、NB、SVM及ANN六种模型。结果表明，K-NN和J48在检测精度与测试时间上综合表现最佳。

入侵检测；NSL-KDD；UNSW-NB15；K-近邻；支持向量机；机器学习；Weka；朴素贝叶斯；决策树；随机森林

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