RF Jamming Dataset: A Wireless Spectral Scan Approach for Malicious Interference Detection

The evolution of next-generation communication systems demands that wireless networks possess the attributes of awareness, adaptability, and intelligence. Wireless sensing techniques provide valuable information about the radio signals in the environment. However, hostile threats, such as jamming, eavesdropping, and manipulation, pose significant challenges to these networks. This paper presents a comprehensive study of an innovative RF-jamming detection testbed designed to combat these threats. The testbed leverages the spectral scan capability of the wireless network interfaces and the jamming toolkit, JamRF, to accurately detect and mitigate jamming attacks. This study outlines the methodology used to develop the testbed, along with a detailed discussion on the rationales behind the design decisions. The accompanying RF jamming dataset, which comprises experimentally measured data, is expected to promote the development and evaluation of jamming detection and avoidance systems. As a proof-of-concept, we trained five different machine learning algorithms and achieved a jamming detection accuracy of over 90% for all algorithms. The proposed RF jamming dataset and testbed represent a significant advancement in the fight against malicious interference in wireless networks.