Conference Proceedings

Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning

S Weerasinghe, SM Erfani, T Alpcan, C Leckie, J Riddle

2018 IEEE 43rd Conference on Local Computer Networks (LCN) | IEEE | Published : 2019


Software-defined radios (SDRs) with substantial cognitive (computing) and networking capabilities provide an opportunity for observing radio communications in an area and potentially identifying malicious rogue agents. Assuming a prevalence of encryption methods, a cognitive network of such SDRs can be used as a low-cost and flexible scanner/sensor array for distributed detection of anomalous communications by focusing on their statistical characteristics. Identifying rogue agents based on their wireless communications patterns is not a trivial task, especially when they deliberately try to mask their activities. We address this problem using a novel framework that utilizes adversarial learn..

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Awarded by Australian Research Council

Funding Acknowledgements

This work was supported in part by the Australian Research Council Discovery Project under Grant DP140100819 and by a grant from the Northrop Grumman Corporation.