利用堆叠机器学习模型和优化来改进网络攻击检测和检测
Neha Pramanick1, Jimson Mathew1, Shitharth Selvarajan2,3,4
1Computer Science and Engineering, IIT Patna, Patna, Bihar, 801103, India.
Scientific reports
|May 14, 2025
概括
本研究介绍了使用机器学习的增强入侵检测系统 (IDS) 框架. 这种新的方法提高了检测网络攻击的准确性和效率,特别是在数据不平衡的情况下.
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:
- 复杂的网络攻击需要先进的入侵检测系统 (IDS).
- 现有的方法在高维数据,不平衡的类和高错误阳性率方面扎.
- 强大而准确的IDS对于网络防御至关重要.
研究的目的:
- 引入一种用于入侵检测的创新框架.
- 解决当前IDS的准确性,不平衡数据和效率方面的挑战.
- 通过整合新的机器学习技术,开发出优质的IDS.
主要方法:
- 集成J48和ExtraTreeClassifier机器学习模型进行分类.
- 一个改进的平衡优化器 (EEO) 用于使用K-Nearest Neighbors (KNN) 费舍尔和准确度得分进行特征选择.
- 合成少数群体过量采样技术与代分区过器 (SMOTE-IPF) 用于类平衡和KNN用于数据归算.
主要成果:
- 获得了高精度 (NSL-KDD上99.7%,UNSW-NB15上98.1%) 和F1分数 (分别为99.6%和98.0%).
- 在特征选择精度和分类准确度方面表现出卓越的性能.
- 有效地处理了少数类实例,并提高了计算效率.
结论:
- 拟议的IDS框架显著提高了检测能力.
- 该方法提供了一个强大的解决方案,用于管理不平衡的数据集和减少假阳性.
- 该系统为网络入侵检测提供了计算效率高,准确的方法.
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