Mitigating RF jamming attacks at the physical layer with machine learning
Mitigating RF jamming attacks at the physical layer with machine learning
复制标题
通过机器学习减轻物理层的射频干扰攻击
DOI：
10.1049/cmu2.12461
复制
发表时间：
2022
影响因子：
1.6
通讯作者：
Dandekar, Kapil R.
中科院分区：
文献类型：
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作者：
Jacovic, Marko;Rey, Xaime Rivas;Mainland, Geoffrey;Dandekar, Kapil R.
由于无线系统在我们日常生活中的广泛使用，必须保护无线通信设备免受恶意威胁，包括主动干扰攻击。干扰缓解技术主要通过仿真或针对非常具体的干扰条件使用硬件进行评估。本文介绍了一个实验性的软件定义的基于无线电的射频干扰缓解平台，该平台执行在线干扰分类，并在物理层利用可重构波束控制天线。提出并验证了一种射线跟踪仿真系统，以实现复杂的室外和移动的特定场景的硬件在环干扰实验。随机森林分类器基于空中收集的数据进行训练，并集成到平台中。缓解系统在空中和光线跟踪仿真环境中进行评估。实验结果突出了在存在有源干扰攻击的情况下使用干扰缓解系统的好处。
Wireless communication devices must be protected from malicious threats, including active jamming attacks, due to the widespread use of wireless systems throughout our every‐day lives. Jamming mitigation techniques are predominately evaluated through simulation or with hardware for very specific jamming conditions. In this paper, an experimental software defined radio‐based RF jamming mitigation platform which performs online jammer classification and leverages reconfigurable beam‐steering antennas at the physical layer is introduced. A ray‐tracing emulation system is presented and validated to enable hardware‐in‐the‐loop jamming experiments of complex outdoor and mobile site‐specific scenarios. Random forests classifiers are trained based on over‐the‐air collected data and integrated into the platform. The mitigation system is evaluated for both over‐the‐air and ray‐tracing emulated environments. The experimental results highlight the benefit of using the jamming mitigation system in the presence of active jamming attacks.
基于软件定义无线电构建的实时协议感知反应干扰框架
DOI：
10.1145/2627788.2627798
发表时间：
2014
期刊：
SRIF 2014 - Proceedings of the ACM SIGCOMM 2014 Workshop on Software Radio Implementation Forum
影响因子：
--
作者：
Nguyen, Danh;Sahin, Cem;Shishkin, Boris;Kandasamy, Nagarajan;Dandekar, Kapil R.
通讯作者：
Dandekar, Kapil R.
基于人工神经网络的干扰检测：测量研究
DOI：
10.1145/3324921.3328788
发表时间：
2019
期刊：
Proceedings of the ACM Workshop on Wireless Security and Machine Learning
影响因子：
--
作者：
Selen Gecgel;Caner Goztepe;Günes Karabulut
通讯作者：
Günes Karabulut