一个新的混合饥饿游戏算法用于基于非线性回归建模的入侵检测系统
Shahriar Mohammadi1, Mehdi Babagoli1
1Industrial Engineering Department, KN Toosi University of Technology, Tehran, Iran.
概括
这项研究介绍了一种新的入侵检测系统,使用人工智能来打击复杂的网络威胁. 混合算法达到99.17%的准确性,大大提高了网络安全,减少了虚假报警.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络入侵检测 网络入侵检测
背景情况:
- 越来越复杂的网络威胁需要先进的基于异常的入侵检测系统 (AIDS).
- 现有的艾滋病面临着诸多挑战,包括高错误报警率,过时的数据集和不够的准确性.
- 人工智能为增强艾滋病能力提供了潜在的解决方案.
研究的目的:
- 提出一种新的入侵检测系统,以有效和准确地检测各种网络攻击.
- 解决当前艾滋病方法的局限性,例如不平衡的数据和特征选择.
主要方法:
- 使用Smote-Tomek链接进行CICIDS数据集的数据平衡和预处理.
- 开发了一种混合的元启发算法,将灰狼,饥饿游戏搜索 (HGS) 和遗传算法运算符结合起来,用于特征选择和攻击检测.
- 使用非线性二次回归优化混合HGS算法建模网络行为.
主要成果:
- 拟议的特征选择技术删除了超过80%的无关特征.
- 与基线算法相比,混合HGS算法表现出优越的性能.
- 实现了99.17%的平均测试准确率,超过了基线算法的94.61%的性能.
结论:
- 这种新的混合AI驱动入侵检测系统有效地以高准确度缓解网络威胁.
- 拟议的方法对现有的网络安全技术提供了显著的改进.
- 该系统成功地解决了数据预处理,特征选择和检测准确性的挑战.
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