A Probability‐Aware AI Framework for Reliable Anti‐Jamming Communication

ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐hopping techniques. Nevertheless, these techniques become vulnerable when facing intelligent jammers. To address this issue, we introduce a new framework that integrates Siamese neural networks with a dual‐probability‐attention mechanism (DPAM) to provide reliable anti‐jamming communication and robust protection. This framework contains several components, which are (1) twin neural networks to execute coordinated cryptographic adaptation operation using a contrastive learning approach, (2) a DPAM module to analyse signals using probability encoding and dual temporal‐spectral attention to enhance accurate recognition, (3) adversarial training to counter growing attack patterns and (4) a lightweight neural encryption module that is developed to provide real‐time operation. Internal DPAM architecture combines probability distributions with Bayesian attention fusion. This combination increases the detection by 23% when compared to other attention mechanisms. Conducted simulation evaluations on a public dataset shows that the frameworks reached an accuracy of 98.7%, whereas other reinforcement learning (RL) methods achieved 82%. In addition, 45% reduction in latency was reached when compared to frequency‐hopping solutions. Furthermore, the solution got up to 96% resilience against attacks.