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The security and privacy of underwater acoustic sensor networks has received extensive attention recently due to the proliferation of underwater activities. This paper proposes an analytical model to investigate the eavesdropping attacks in underwater acoustic sensor networks. Our analytical framework considers the impacts of various underwater acoustic(More)
Wireless sensor networks (WSNs) play an important role in Cyber Physical Social Sensing (CPSS) systems. An eavesdropping attack is one of the most serious threats to WSNs since it is a prerequisite for other malicious attacks. In this paper, we propose a novel anti-eavesdropping mechanism by introducing friendly jammers to wireless sensor networks (WSNs).(More)
Context identifying based on speech data is important to social services and city management. In a complex application environment, a speech recognition system needs to address two main problems: background noises and large vocabulary search latency. We use the adjustment acoustic model to deal with the scenario adaptation, and we use adjustment dictionary(More)
This paper investigates the eavesdropping attacks in underwater acoustic networks (UANets). In particular, we propose an analytical framework to model the eavesdropping attacks in UANets in terms of the eavesdropping probability. Results of extensive simulations match the analytical results, indicating the effectiveness and accuracy of our model. Besides,(More)
This paper proposes a novel anti-eavesdropping scheme by introducing artificial noise caused by friendly jammers deployed in wireless networks. In particular, we propose an analytical model to quantify the eavesdropping risk of wireless network with friendly jammers. Our approach considers both large-scale path loss and Rayleigh fading. Our numerical(More)