Electrical and Electronic Engineering - Research Publications

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Author: Leckie, Christopher × Author: Alpcan, Tansu ×

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Now showing 1 - 8 of 8
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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 (IEEE, 2023)
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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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    Generative Adversarial Networks for anomaly detection on decentralised data
    Katzefa, M ; Cullen, AC ; Alpcan, T ; Leckie, C (PERGAMON-ELSEVIER SCIENCE LTD, 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.
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    Support vector machines resilient against training data integrity attacks
    Weerasinghe, S ; Erfani, SM ; Alpcan, T ; Leckie, C (Elsevier BV, 2019-12-01)
    Support Vector Machines (SVMs) are vulnerable to integrity attacks, where malicious attackers distort the training data in order to compromise the decision boundary of the learned model. With increasing real-world applications of SVMs, malicious data that is classified as innocuous may have harmful consequences. This paper presents a novel framework that utilizes adversarial learning, nonlinear data projections, and game theory to improve the resilience of SVMs against such training-data-integrity attacks. The proposed approach introduces a layer of uncertainty through the use of random projections on top of the learners, making it challenging for the adversary to guess the specific configurations of the learners. To find appropriate projection directions, we introduce novel indices that ensure the contraction of the data and maximize the detection accuracy. Experiments with benchmark data sets show increases in detection rates up to 13.5% for OCSVMs and up to 14.1% for binary SVMs under different attack algorithms when compared with the respective base algorithms.