A New Intelligent Jamming Attacks Detection using FCM clustering technique Based on Data Mining for Wireless Communication

This work proposes a new intelligent jamming attacks detector Fuzzy C Means (FCM) clustering combined with MWC via cooperative sensing based sub-Nyquist sampling theory. The compressed sampling (CS) data matrix obtained after MWC via cooperative is considered as the input of the intelligent FCM abnormal detector in fusion center (FC). In the presence of cooperative pulse jammers and assuming Additive White Gaussian noise (AWGN), we propose two hypotheses to differentiate between the presence and absence of malicious attackers in the system. This new intelligent data mining detector proposed in this work is based on the characteristics of FCM clustering. The performance of the proposed system shows good results about intelligent detection of the abnormal behavior of patterns in the case of presence of cooperative jammers.