Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies
This addresses a critical security issue in wireless communications for scenarios with dynamic or multiple jammers, representing a domain-specific improvement over existing methods.
The paper tackles the problem of defending against multiple or varying jamming policies in wireless networks by proposing an anti-jamming method that adapts to current attacks and uses recurrent neural networks to model interactions, achieving successful transmission rates above 75% and ergodic rates above 80% when 70% of the spectrum is jammed.
Conventional anti-jamming methods mainly focus on preventing single jammer attacks with an invariant jamming policy or jamming attacks from multiple jammers with similar jamming policies. These anti-jamming methods are ineffective against a single jammer following several different jamming policies or multiple jammers with distinct policies. Therefore, this paper proposes an anti-jamming method that can adapt its policy to the current jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming method that estimates the future occupied channels using the jammers' occupied channels in previous time slots is proposed. In both single and multiple jammers scenarios, the interaction between the users and jammers is modeled using recurrent neural networks (RNN)s. The performance of the proposed anti-jamming methods is evaluated by calculating the users' successful transmission rate (STR) and ergodic rate (ER), and compared to a baseline based on Q-learning (DQL). Simulation results show that for the single jammer scenario, all the considered jamming policies are perfectly detected and high STR and ER are maintained. Moreover, when 70 % of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves an STR and ER greater than 75 % and 80 %, respectively. These values rise to 90 % when 30 % of the spectrum is under jamming attacks. In addition, the proposed anti-jamming methods significantly outperform the DQL method for all the considered cases and jamming scenarios.