Learning-Based Jamming Attack against Low-Duty-Cycle Networks

Jamming is a typical attack by exploiting the nature of wireless communication. Lots of researchers are working on improving energy-efficiency of jamming attack from the attacker’s view. Whereas, in the low-duty-cycle wireless sensor networks where nodes stay asleep most of time, the design of jamming attack becomes even more challenging especially when considering the stochastic transmission pattern arising from both the clock drift and other uncertainties. In this paper, we propose LearJam, a novel learning-based jamming attack strategy against low-duty-cycle networks, which features the two-phase design consisting of the learning phase and attacking phase. Then in order to degrade the network throughput to the maximal degree, LearJam jointly optimizes these two phases subject to the energy constraint. Moreover, such process of optimization is operated iteratively to accommodate the requirement of practical implementation. Conversely, we also discuss how the state-of-the-art mechanisms can defend against LearJam, which will aid the researchers to improve the security of low-duty-cycle networks. Extensive simulations show that our design achieves significantly higher number of successful attacks and reduces the network’s throughput considerably, especially in a sparse low-duty-cycle network, compared with some typical jamming strategies.