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Wireless communications are particularly vulnerable to eavesdropping attacksdue to their broadcast nature. To effectively deal with eavesdroppers, existingsecurity techniques usually require accurate channel state information (CSI),e.g., for friendly jamming (FJ), and/or additional computing resources attransceivers, e.g., cryptography-based solutions, which unfortunately may notbe feasible in practice. This challenge is even more acute in low-end IoTdevices. We thus introduce a novel deep learning-based FJ framework that caneffectively defeat eavesdropping attacks with imperfect CSI and even withoutCSI of legitimate channels. In particular, we first develop anautoencoder-based communication architecture with FJ, namely AEFJ, to jointlymaximize the secrecy rate and minimize the block error rate at the receiverwithout requiring perfect CSI of the legitimate channels. In addition, to dealwith the case without CSI, we leverage the mutual information neural estimation(MINE) concept and design a MINE-based FJ scheme that can achieve comparablesecurity performance to the conventional FJ methods that require perfect CSI.Extensive simulations in a multiple-input multiple-output (MIMO) systemdemonstrate that our proposed solution can effectively deal with eavesdroppingattacks in various settings. Moreover, the proposed framework can seamlesslyintegrate MIMO security and detection tasks into a unified end-to-end learningprocess. This integrated approach can significantly maximize the throughput andminimize the block error rate, offering a good solution for enhancingcommunication security in wireless communication systems.

Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI