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Xing, Junchi, Yang, Mingliang, Zhou, Haifeng, Wu, Chunming, Ruan, Wei.  2019.  Hiding and Trapping: A Deceptive Approach for Defending against Network Reconnaissance with Software-Defined Network. 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). :1—8.

Network reconnaissance aims at gathering as much information as possible before an attack is launched. Meanwhile, static host address configuration facilitates network reconnaissance. Currently, more sophisticated network reconnaissance has been emerged with the adaptive and cooperative features. To address this, in this paper, we present Hiding and Trapping (HaT), which is a deceptive approach to disrupt adversarial network reconnaissance with the help of the software-defined networking (SDN) paradigm. HaT is able to hide valuable hosts from attackers and to trap them into decoy nodes through strategic and holistic host address mutation according to characteristic of adversaries. We implement a prototype of HaT, and evaluate its performance by experiments. The experimental results show that HaT is capable to effectively disrupt adversarial network reconnaissance with better deceptive performance than the existing address randomization approach.