Secure Wireless Communication Using Support Vector Machines

We consider a wireless system consisting of $K$ legal users, one access point (AP)and one active eavesdropper. The eavesdropper is assumed to attack the system in the uplink phase. Focusing on intrusion detection, we introduce a framework to create datasets that are then put into support vector machine (SVM)classifiers. The characteristics of the three features (i.e., MEAN, RATIO and SUM)in our datasets are formulated from post-processing signals. Based on the three defined features, artificial training data (ATD)is also formed and used at the AP. By training SVM models, we show the high feasibility of detecting active eavesdroppers in many cases. The performance of our proposed approach is evaluated in terms of accuracy and through numerical examples.

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