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基于对抗性的条件表式生成入侵检测系统,用于检测Ddos和Dos攻击的物联网网络
Basim Ahmad Alabsi1, Mohammed Anbar2, Shaza Dawood Ahmed Rihan1
1Applied College, Najran University, Kind Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
一个新的入侵检测系统 (IDS) 使用条件表式生成对抗网络 (CTGAN) 来检测物联网 (IoT) 网络上的分布式拒绝服务 (DDoS) 和拒绝服务 (DoS) 攻击,提高检测准确度.
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) 设备的普及加剧了威胁格局,特别是在分布式拒绝服务 (DDoS) 和拒绝服务 (DoS) 攻击方面.
- 这些攻击带来了重大风险,导致服务中断和对依赖物联网基础设施的组织造成财务影响.
研究的目的:
- 引入一种新的入侵检测系统 (IDS),专门设计用于在物联网环境中识别DDoS和DoS攻击.
- 利用条件表式生成对抗网络 (CTGAN) 提高攻击检测模型的性能.
主要方法:
- 开发基于CTGAN的IDS,使用生成器创建模仿合法模式的合成流量数据,以及区分器来区分恶意流量.
- 利用CTGAN合成生成的表格数据来训练和提高各种浅层机器学习和深度学习分类器的性能.
- 使用Bot-IoT数据集评估拟议的IDS,评估关键性能指标,如检测准确度,精度,回忆和F1分数.
主要成果:
- 拟议的基于CTGAN的IDS在物联网网络中展示了对DDoS和DoS攻击的准确检测能力.
- 实验结果证实了该方法在识别恶意网络流量的有效性.
- 该研究强调了CTGAN在提高机器学习和深度学习分类器用于入侵检测的性能方面的实质性积极影响.
结论:
- 开发的基于CTGAN的IDS提供了一个强大的解决方案,用于检测物联网网络中的DDoS和DoS攻击.
- 整合CTGAN显著提高了机器学习和深度学习模型在识别网络威胁方面的有效性.
- 这种方法有助于加强物联网生态系统的安全性和可靠性,防止普遍的网络攻击.
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