Machine learning-based anti-jamming technique at the physical layer

The reliance on wireless services to exchange critical data is associated with various threats and attacks, which must be mitigated to ensure integrity and security of those wireless services. Posing a serious challenge to wireless systems, jamming is among these attacks. In order to mitigate jamming, directive antennas are used to minimize the signals that are received from the jammer, while maximizing the received legitimate signal from the authorized transmitter. In this paper, we propose a machine learning-based anti-jamming framework to provide a spatially dynamic and instantaneous anti-jamming performance that is achieved at the physical layer. The proposed framework incorporates a dataset that can be deployed in the hardware of a receiver with a massive Multiple-Inputs Multiple-Outputs-(MIMO) antenna. Our extensive performance evaluation results demonstrate the effective performance of the proposed framework in preserving integrity of a massive-MIMO communication system despite the presence of a hostile jammer. Particularly, due to the tabular nature of the generated dataset, tree-based random forest models achieved the best performance with a signal-to-interference-plus-noise ratio accuracy of 92%$$ 92\% $$ and fast anti-jamming response in around 1.66 s under sever jamming conditions.