Deep Learning for Launching and Mitigating Wireless Jamming Attacks

Understand

An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented.

• A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them.
• This jamming approach is shown to reduce the transmitter's performance much more severely compared with random or sensing-based jamming.
• The deep learning classification scores are used by the jammer for power control subject to an average power constraint.