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.

ZigBee has been widely recognized as an important enabling technique for Internet of Things (IoT). However, the ZigBee nodes are normally resource-limited, making the network susceptible to a variety of security threats. This paper closely investigates a severe attack on ZigBee networks termed as ghost, which leverages the underlying vulnerabilities of the IEEE 802.15.4 security suites to deplete the energy of the nodes. We show that the impact of ghost is very large and that it can facilitate a variety of threats including denial of service and replay attacks. We highlight that merely deploying a standard suite of advanced security techniques does not necessarily guarantee improved security, but instead might be leveraged by adversaries to cause severe disruption in the network. We propose several recommendations on how to localize and withstand the ghost and other related attacks in ZigBee networks. Extensive simulations are provided to show the impact of the ghost and the performance of the proposed recommendations. Moreover, physical experiments also have been conducted and the observations confirm the severity of the impact by the ghost attack. We believe that the presented work will aid the researchers to improve the security of ZigBee further.

Low-duty-cycle network plays an crucial role in improving energy efficiency of wireless communication, where nodes stay asleep most of time. Despite energy saving, the security of low-duty-cycle networks is of great concern. The attacking strategy design becomes even more challenging considering the stochastic transmission patterns arising from both the clock drift and other uncertainties. In this paper, we propose LearJam, a novel two-phase energy-efficient learning-based jamming attack strategy against low-duty-cycle networks, where the attacker estimates the distribution of transmission period in the learning phase, and schedules its jamming attacks in the attacking phase based on this estimated distribution. We jointly optimize the learning duration and the attacking duration under the energy constraint in order to degrade the network throughput to the maximal degree. We propose simple yet effective methods to solve both the single-node and multi-node scenarios. We further discuss a state-of-the-art mechanism defending against LearJam by re-scheduling transmission pattern, 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 (increasing 38%-762%) in a sparse low-duty-cycle network compared with some traditional jamming strategies.