通过异常流量检测保护物联网通信:遗传算法和合集方法的协同作用
Behnam Seyedi1, Octavian Postolache1
1Department of Science, Instituto de Telecomunicacoes, ISCTE-University Institute of Lisbon, 1649-026 Lisbon, Portugal.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了物联网 (IoT) 网络的先进异常检测框架. 基于机器学习的系统通过准确识别和防止物联网生态系统中的网络威胁来提高安全性.
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:
- 物联网 (IoT) 系统面临着严重的安全挑战,原因是去中心化架构和资源有限的设备.
- 异常的网络行为和数据操纵威胁着物联网的安全性和可靠性.
- 机器学习方法越来越多地用于物联网中的入侵检测和预防.
研究的目的:
- 为物联网网络提出一个先进的多相异常检测框架.
- 加强物联网生态系统对网络威胁的安全性和可靠性.
- 为各种物联网场景开发可扩展和适应的解决方案.
主要方法:
- 使用Median-KS测试进行数据预处理,以减少噪声和数据平衡.
- 通过带有灵感的搜索策略的遗传算法进行最佳特征选择.
- 组合分类器结合了决策树,随机森林和XGBoost算法.
主要成果:
- 获得了98%的准确性,比现有方法提高了12.5%.
- 检测率提高到95% (14%的改善).
- 将虚假阳性率降至10% (9.3%的降低),虚假阴性率降至5% (10.8%的降低).
结论:
- 拟议的框架证明了对保护物联网网络的卓越有效性,可靠性和可扩展性.
- 多步骤的方法确保了应对多样化和不断变化的网络威胁的适应性.
- 结果强调了框架对现实世界物联网安全应用的潜力.
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