计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 459-463.doi: 10.11896/jsjkx.200600161
曹扬晨1, 朱国胜1, 祁小云2, 邹洁1
CAO Yang-chen1, ZHU Guo-sheng1, QI Xiao-yun2, ZOU Jie1
摘要： 为了有效地检测网络的攻击行为,机器学习被广泛用于对不同类型的入侵检测进行分类,传统的决策树方法通常用单个模型训练数据,容易出现泛化误差大、过拟合的问题。为解决该问题,文中引入并行式集成学习的思想,提出基于随机森林的入侵检测模型,由于随机森林中每棵决策树都有决策权,因此可以很好地提高分类的准确性。利用NSL-KDD数据集对入侵检测模型进行训练和测试,实验结果表明,该模型的准确率可达99.91%,具有非常好的入侵检测分类效果。
中图分类号:
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