集成平均深度神经网络用于异质物联网设备中的尸网络检测
Aulia Arif Wardana1, Grzegorz Kołaczek2, Arkadiusz Warzyński2
1Wrocław University of Science and Technology, Wrocław, Poland. aulia.wardana@pwr.edu.pl.
Scientific reports
|February 16, 2024
概括
本研究介绍了用于物联网 (IoT) 网络的增强入侵检测系统 (IDS),有效地检测使用集体深度神经网络 (DNN) 的各种设备上的尸网络攻击. 这种新的方法在复杂的物联网环境中识别恶意活动时实现了高精度.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 尸网络攻击对物联网 (IoT) 设备构成重大威胁,导致数据泄露和网络中断.
- 现有的基于网络的入侵检测系统 (NIDS) 难以应对物联网环境的动态和异质性.
- 物联网设备类型,配置和供应商的多样性使有效的威胁检测变得复杂.
研究的目的:
- 提出一个新的入侵检测系统 (IDS),专门为异质物联网环境设计.
- 通过利用深度神经网络 (DNN) 的合并方法来增强尸网络攻击的检测.
- 解决物联网安全中设备多样性所带来的挑战.
主要方法:
- 开发了一个IDS,将异质物联网设备的流量数据组合在一起.
- 利用深度神经网络 (DNN) 为每个设备类型创建个别训练模型.
- 实施了集成平均化方法,将来自单个DNN模型的预测结合起来进行最终检测.
- 使用N-BaIoT数据集验证了拟议的IDS.
主要成果:
- 整体平均DNN模型在检测尸网络攻击方面表现出高效.
- 在识别尸网络活动时获得了97.21%的平均准确性.
- 获得的精度为91.41%,回忆率为87.31%,F1得分为88.48%.
结论:
- 集合平均DNN为检测异质物联网网络中的尸网络攻击提供了强大的解决方案.
- 拟议的IDS有效地克服了动态物联网环境中的传统NIDS的局限性.
- 这项研究在保护各种物联网生态系统免受协调的网络威胁方面取得了重大进展.
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