使用基于决策树的近距离政策优化算法有效检测恶意流量:深度强化学习恶意流量检测模型,结合值
Yuntao Zhao1, Deao Ma1, Wei Liu1
1School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China.
Entropy (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了一种新的恶意流量检测模型,使用决策树和深度强化学习 (近接政策优化). 该模型在识别网络威胁方面实现了高精度,增强了网络安全.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络入侵检测 网络入侵检测
背景情况:
- 网络攻击对个人,企业和国家构成重大威胁.
- 越来越依赖互联网,需要先进的网络入侵检测技术.
研究的目的:
- 构建一个恶意流量检测模型.
- 提高网络入侵检测系统的准确性和效率.
主要方法:
- 使用基于信息的决策树分类器来进行特征选择.
- 实施了深度强化学习近接政策优化 (PPO) 算法用于检测.
- 为了改进更新,在PPO算法中引入了一个规则性术语.
主要成果:
- 开发的模型在CIC-IDS2017数据集上实现了99.17%的二进制分类准确度.
- 来自决策树的特征重要性得分被用来删除贡献较少的特征.
- 深度强化学习算法的持续训练和参数更新导致了一个高度准确的检测模型.
结论:
- 决策树和近接政策优化 (PPO) 的集成为恶意流量检测提供了一个强大的方法.
- 拟议的模型在识别网络入侵方面表现出卓越的性能.
- 这种方法在网络安全和威胁检测领域取得了重大进展.
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