Xuran Li, Jianlong Xu, Hong-Ning Dai, Qinglin Zhao, Chak Fong Cheang, Qiu Wang
Journal of Computational Science, Vol. 11, Pages 196 - 204, November 2015
Publication year: 2015

This paper concerns the eavesdropping attacks from the eavesdroppers’ perspective, which is new since most of current studies consider the problem from the good nodes’ perspective. In this paper, we originally propose an analytical framework to quantify the effective area and the probability of the eavesdropping attacks. This framework enables us to theoretically evaluate the impact of node density, antenna model, and wireless channel model on the eavesdropping attacks. We verify via extensive simulations that the proposed analytical framework is very accurate. Our results show that the probability of eavesdropping attacks significantly vary, depending on the wireless environments (such as shadow fading effect, node density, and antenna types). This study lays the foundation toward preventing the eavesdropping attacks in more effective and more economical ways.

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