强大的基因机器学习组合模型用于网络流量中的入侵检测
Muhammad Ali Akhtar1, Syed Muhammad Owais Qadri2, Maria Andleeb Siddiqui3
1Department of Computer and Information System Engineering, NED University of Engineering and Technology, Karachi, Pakistan.
Scientific reports
|October 11, 2023
概括
这项研究引入了用于网络入侵检测的新型强大的基因组合分类器,与现有的机器学习方法相比,显著提高了准确性和减少了错误. 增强的算法为检测网络流量中的网络威胁提供了更可靠的解决方案.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 互联网和通信技术的快速发展导致了网络攻击的增加,压倒了传统的网络安全系统.
- 目前的入侵检测系统 (IDS) 面临着虚假警报,无法自主预防攻击,难以检测新型入侵.
- 基于机器学习 (ML) 的IDS正在成为有效的网络入侵检测的有希望的解决方案.
研究的目的:
- 通过预处理和组合方法开发高度可靠的算法来增强网络入侵检测.
- 对其他机器学习组合算法进行强大的基因组合分类器的性能评估.
- 解决现有IDS在准确性,错误报警率和新威胁检测方面的局限性.
主要方法:
- 使用了与四个强大的机器学习合并算法结合的数据分析技术:投票分类器,包装分类器,梯度增强分类器和基于随机森林的包装.
- 使用网络数据集为每个算法开发和测试模型.
- 提出并评估了一种新的强大的遗传组合分类器.
主要成果:
- 与其他测试方法相比,拟议的强大的遗传组合分类器表现出优越的性能.
- 建议的算法实现了平均平方误差 (MSE) 和平均绝对误差 (MAE) 的最低值.
- 性能图表证实了算法在预测异常事件中的有效性.
结论:
- 强大的遗传组合分类器是增强网络入侵检测的高效方法.
- 拟议的方法在准确性和可靠性方面比现有的机器学习技术有了显著的改进.
- 未来的工作可以探索整合更多的机器学习集合分类器和深度学习技术,以进一步进步.
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