- Electrical and Electronic Engineering - Research Publications
Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableA Group Formation Game for Local Anomaly DetectionYe, Z ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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ItemNo Preview AvailableOnline Trajectory Anomaly Detection Based on Intention OrientationWang, C ; Erfani, S ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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ItemNo Preview AvailableRobust Wireless Network Anomaly Detection with Collaborative Adversarial AutoencodersKatzef, 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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ItemNo Preview AvailableWireless Network Simulation to Create Machine Learning Benchmark DataKatzef, M ; Cullen, AC ; Alpcan, T ; Leckie, C ; Kopacz, J (IEEE, 2022-01-01)
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ItemNo Preview AvailableLocal Intrinsic Dimensionality Signals Adversarial PerturbationsWeerasinghe, S ; Abraham, T ; Alpcan, T ; Erfani, SM ; Leckie, C ; Rubinstein, BIP (IEEE, 2022)
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ItemAnomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal FeaturesYang, M ; Rajasegarar, S ; Rao, AS ; Leckie, C ; Palaniswami, M ; Shi, Z ; Vadera, S ; Li, G (Springer, 2016)important problem in real-life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio-temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.
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ItemCluster-based Crowd Movement Behavior DetectionYang, M ; Rashidi, L ; Rao, AS ; Rajasegarar, S ; Ganji, M ; Palaniswami, M ; Leckie, C ; Murshed, M ; Paul, M ; Asikuzzaman, M ; Pickering, M ; Natu, A ; RoblesKelly, A ; You, S ; Zheng, L ; Rahman, A (IEEE, 2019-01-01)Crowd behaviour monitoring and prediction is an important research topic in video surveillance that has gained increasing attention. In this paper, we propose a novel architecture for crowd event detection, which comprises methods for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events. In our proposed framework, we use clusters to represent the groups of objects/people present in the scene. We then extract the movement patterns of the various groups of objects over the video sequence to detect movement patterns. We define several crowd events and propose a methodology to detect the change point of the group events over time. We evaluated our scheme using six video sequences from benchmark datasets, which include events such as walking, running, global merging, local merging, global splitting and local splitting. We compared our scheme with state of the art methods and showed the superiority of our method in accurately detecting the crowd behavioral changes.
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ItemCrowd Activity Change Point Detection in Videos via Graph Stream MiningYang, M ; Rashidi, L ; Rajasegarar, S ; Leckie, C ; Rao, AS ; Palaniswami, M (IEEE, 2018)In recent years, there has been a growing interest in detecting anomalous behavioral patterns in video. In this work, we address this task by proposing a novel activity change point detection method to identify crowd movement anomalies for video surveillance. In our proposed novel framework, a hyperspherical clustering algorithm is utilized for the automatic identification of interesting regions, then the density of pedestrian flows between every pair of interesting regions over consecutive time intervals is monitored and represented as a sequence of adjacency matrices where the direction and density of flows are captured through a directed graph. Finally, we use graph edit distance as well as a cumulative sum test to detect change points in the graph sequence. We conduct experiments on four real-world video datasets: Dublin, New Orleans, Abbey Road and MCG Datasets. We observe that our proposed approach achieves a high F-measure, i.e., in the range [0.7, 1], for these datasets. The evaluation reveals that our proposed method can successfully detect the change points in all datasets at both global and local levels. Our results also demonstrate the efficiency and effectiveness of our proposed algorithm for change point detection and segmentation tasks.
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ItemDetection of Anomalous Communications with SDRs and Unsupervised Adversarial LearningWeerasinghe, 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.