Active Eavesdropping Attacks Detection in Massive Multiple Input Multiple Output Based on Machine Learning

In this paper, we develop a machine learning model to detect active eavesdroppers in a Massive Multiple Input Multiple Output (MIMO) system. Massive MIMO systems are naturally immune to passive eavesdroppers, but this is dramatically degraded by active eavesdroppers. We propose two machine learning-based schemes, i.e. a Support Vector Machine (SVM) based scheme and a Naive-Bayes (NB) based scheme, to classify and detect the presence of an active eavesdropper. Then, we apply a Deep Neural Network (DNN) for detecting the presence of an active eavesdropper. We first build structured datasets based on the Received Signal Strength (RSS) and then apply SVM classifiers, NB classifiers, and DNN to those structured datasets. We built a machine learning model based on a realistic scenario where the Channel State Information (CSI) of the channels (legitimate users and eavesdroppers) is unknown. We exploit the massive MIMO technique features to improve the performance of the detection models. The work presented here provides insights into the design of DNN and new machine learning-based secure transmission schemes in Massive MIMO.