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Dai, Hong-Ning, Wang, Hao, Xiao, Hong, Zheng, Zibin, Wang, Qiu, Li, Xuran, Zhuge, Xu.  2016.  On Analyzing Eavesdropping Behaviours in Underwater Acoustic Sensor Networks. Proceedings of the 11th ACM International Conference on Underwater Networks & Systems. :53:1–53:2.
Underwater Acoustic Sensor Networks (UWASNs) have the wide of applications with the proliferation of the increasing underwater activities recently. Most of current studies are focused on designing protocols to improve the network performance of WASNs. However, the security of UWASNs is also an important concern since malicious nodes can easily wiretap the information transmitted in UWASNs due to the vulnerability of UWASNs. In this paper, we investigate one of security problems in UWASNs - eavesdropping behaviours. In particular, we propose a general model to quantitatively evaluate the probability of eavesdropping behaviour in UWASNs. Simulation results also validate the accuracy of our proposed model.
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Zhang, Yihan, Wu, Jiajing, Chen, Zhenhao, Huang, Yuxuan, Zheng, Zibin.  2019.  Sequential Node/Link Recovery Strategy of Power Grids Based on Q-Learning Approach. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). :1–5.

Cascading failure, which can be triggered by both physical and cyber attacks, is among the most critical threats to the security and resilience of power grids. In current literature, researchers investigate the issue of cascading failure on smart grids mainly from the attacker's perspective. From the perspective of a grid defender or operator, however, it is also an important issue to restore the smart grid suffering from cascading failure back to normal operation as soon as possible. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing of the failed nodes/links in a sequential fashion. Based on a realistic power flow cascading failure model, we exploit a Q-learning approach to develop a practical and effective policy to identify the optimal way of sequential restorations for large-scale smart grids. Simulation results on three power grid test benchmarks demonstrate the learning ability and the effectiveness of the proposed strategy.