增强的网络入侵检测系统用于物联网安全使用多模态大数据表示与转移学习和游戏理论
Farhan Ullah1,2, Ali Turab1, Shamsher Ullah3
1School of Software, Northwestern Polytechnical University, Xian 710072, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了一种改进的物联网 (IoT) 安全入侵检测系统 (IDS),使用多式联网大数据和传输学习来实现检测网络攻击的98.2%准确性.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 设备容易受到分布式拒绝服务 (DDoS) 和洪水攻击.
- 侵入检测系统 (IDS) 对物联网安全至关重要,但由于众多的网络功能而面临复杂性.
- 有效的攻击检测需要分析各种攻击细节,如引用,类型和主机信息.
研究的目的:
- 开发一个改进的入侵检测系统 (IDS),以提高物联网 (IoT) 的安全性.
- 为了利用多式联网大数据表示和转移学习,实现强大的攻击检测.
- 通过基于游戏理论的过程,正式验证拟议的方法.
主要方法:
- 抓取数据包捕获 (PCAP) 文件以收集攻击数据和字节.
- 使用基于Spark的大数据优化算法来有效处理数据.
- 使用word2vec进行转移学习以提取基于语义的特征.
- 将网络字节转换为图像,并使用基于注意力的剩余网络 (ResNet) 来提取纹理特征.
- 结合文本和纹理特征,用于攻击的多模式分类.
主要成果:
- 拟议的方法在三个物联网数据集 (CIC-IoT 2022,CIC-IoT 2023,Edge-IIoT) 上实现了98.2%的优异分类准确度.
- 使用大数据优化算法对大量数据进行有效处理.
- 成功结合了文本和纹理特征,以改善攻击分类.
结论:
- 开发的IDS有效地提高了对各种网络攻击的物联网安全性.
- 多式联网大数据和转移学习方法为复杂的物联网安全挑战提供了强大的解决方案.
- 游戏理论的整合提供了对系统有效性的正式验证.
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