1. Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning
- Author
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Cherkaoui, S, Andersson, K, AlTurjman, F, Weerasinghe, S, Erfani, SM, Alpcan, T, Leckie, C, Riddle, J, Cherkaoui, S, Andersson, K, AlTurjman, F, Weerasinghe, S, Erfani, SM, Alpcan, T, Leckie, C, and Riddle, J
- Abstract
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 learning, non-linear data transformations to minimize the rogue agent's attempts at masking their activities, and game theory to predict the behavior of rogue agents and take the necessary countermeasures.
- Published
- 2019