基于集体学习的IDS使用投票规则:VEL-IDS
Sura Emanet1, Gozde Karatas Baydogmus1, Onder Demir1
1Marmara University Istanbul, Istanbul, Turkey.
PeerJ. Computer science
|October 9, 2023
概括
这项研究开发了一种使用特征选择和集体学习的先进入侵检测系统 (IDS). 增强的IDS显著提高了攻击检测准确度,并减少了检测时间,以实现强大的计算机安全性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 侵入检测系统 (IDS) 对于分析互联网流量以检测和防止网络攻击至关重要.
- 现有的IDS通常面临着在攻击检测中实现高精度和效率的挑战.
- CIC-CSE-IDS2018数据集为评估IDS性能提供了一个相关的基准.
研究的目的:
- 开发一个高精度和高效率的先进的IDS.
- 为了提高IDS性能,利用特征选择和集体学习技术.
- 为了减少检测时间,同时保持在识别网络入侵的高度准确性.
主要方法:
- 使用特征选择技术进行数据集缩小,如斯皮尔曼相关性,递归特征消除 (RFE) 和千平方测试.
- 实施组合学习方法,结合多个分类器 (例如,额外树,决策树,后勤回归).
- 在CIC-CSE-IDS2018数据集上培训和测试IDS模型.
主要成果:
- 确定显著提高IDS性能的关键功能.
- 与个人分类器方法相比,证明了更高的准确性和更短的检测时间.
- 集体学习模型实现了高准确率,验证了它们的有效性.
结论:
- 开发的IDS,利用特征选择和集体学习,在入侵检测方面取得了重大进展.
- 这些发现突出了组合方法的潜力,以提高IDS的准确性和效率.
- 这项研究有助于提高整体计算机安全性和防止网络威胁.
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