Electrical and Electronic Engineering - Research Publications

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Author: Alpcan, Tansu × Author: Leckie, Christopher × Type: Conference Paper ×

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Now showing 1 - 6 of 6
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    A Group Formation Game for Local Anomaly Detection
    Ye, Z ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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    Robust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders
    Katzef, M ; Cullen, AC ; Alpcan, T ; Leckie, C (Institute of Electrical and Electronics Engineers, 2023)
    Anomaly detection is often deployed in centralised systems, for which critical failure points exist. However, the rising availability of low-cost, wireless-connected devices introduces opportunities for new anomaly detection techniques that leverage more robust topologies. In this paper, we propose a novel collaborative training scheme for anomaly detection models that involves sharing machine learning models amongst devices for incremental training. Using the Adversarial Autoencoder architecture, pseudo-rehearsal, and gossip-based communication, our framework provides all participating devices with a structured representation of other devices' data, so that training can continue even in the event of a device failure, with a 43 % smaller performance degradation than state of the art alternatives. Under both optimal conditions and those with device failure, our model consistently exhibits better anomaly detection performance.
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    Online Trajectory Anomaly Detection Based on Intention Orientation
    Wang, C ; Erfani, S ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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    Wireless Network Simulation to Create Machine Learning Benchmark Data
    Katzef, M ; Cullen, AC ; Alpcan, T ; Leckie, C ; Kopacz, J (IEEE, 2022-01-01)
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    Local Intrinsic Dimensionality Signals Adversarial Perturbations
    Weerasinghe, S ; Abraham, T ; Alpcan, T ; Erfani, SM ; Leckie, C ; Rubinstein, BIP (IEEE, 2022)
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    Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning
    Weerasinghe, S ; Erfani, SM ; Alpcan, T ; Leckie, C ; Riddle, J ; Cherkaoui, S ; Andersson, K ; AlTurjman, F (IEEE, 2019-02-08)
    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.