Abstract
The work in this paper is about to detect and classify jamming attacks in 802.11b wireless networks. The number of jamming detection and classification techniques has been proposed in the literature. Majority of them model individual parameters like signal strength, carrier sensing time, and packet delivery ratio to detect the presence of a jammer and to classify the jamming attacks. The demonstrated results by the authors are often overlapping as most of the jamming regions are closely marked, and they do not help to clearly distinguish different jamming mechanisms. We investigate a multi-modal scheme that models different jamming attacks by discovering the correlation between three parameters: packet delivery ratio, signal strength variation, and pulse width of the received signal. Based on that, profiles are generated in normal scenarios during training sessions which are then compared with test sessions to detect and classify jamming attacks. Our proposed model helps in clearly differentiating the jammed regions for various types of jamming attacks. In addition, it is equally effective for both the protocol-aware and protocol-unaware jammers. The reported results are not based on simulations, but a test-bed was established to experiment real scenarios demonstrating significant enhancements in previous results reported in the literature.