Computing and Information Systems - Theses

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    Anomaly detection in participatory sensing networks
    Anomaly detection or outlier detection aims to identify unusual values in a given dataset. In particular, there is growing interest in collaborative anomaly detection, where multiple data sources submit their data to an online data mining service, in order to detect anomalies with respect to the wider population. By combining data from multiple sources, collaborative anomaly detection aims to improve detection accuracy through the construction of a more robust model of normal behaviour. Cloud-based collaborative architectures such as Participatory Sensing Networks (PSNs) provide an open distributed platform that enables participants to share and analyse their local data on a large scale. Two major issues with collaborative anomaly detection are how to ensure the privacy of participants’ data, and how to efficiently analyse the large-scale high-dimensional data collected in these networks. The first problem we address is the issue of data privacy in PSNs, by introducing a framework for privacy-preserving collaborative anomaly detection with efficient local data perturbation at participating nodes, and global processing of the perturbed records at a data mining server. The data perturbation scheme that we propose enables the participants to perturb their data independently without requiring the cooperation of other parties. As a result our privacy-preservation approach is scalable to large numbers of participants and is computationally efficient. By collecting the participants’ data, the PSN server can generate a global anomaly detection model from these locally perturbed records. The global model identifies interesting measurements or unusual patterns in participants’ data without revealing the true values of the measurements. In terms of privacy, the proposed scheme thwarts several major types of attacks, namely, the Independent Component Analysis (ICA), Distance-inference, Maximum a Posteriori (MAP), and Collusion attacks. We further improve the privacy of our data perturbation scheme by: (i) redesigning the nonlinear transformation to better defend against MAP estimation attacks for normal and anomalous records, and (ii) supporting individual random linear transformations for each participant in order to provide the system with greater resistance to malicious collusion. A notable advantage of our perturbation scheme is that it preserves participants’ privacy while achieving comparable accuracy to non-privacy preserving anomaly detection techniques. The second problem we address in the thesis is how to model and interpret the large volumes of high-dimensional data that are generated in participatory domains by using One-class Support Vector Machines (1SVMs). While 1SVMs are effective at producing decision surfaces for anomaly detection from well-behaved feature vectors, they can be inefficient at modelling the variations in large, high-dimensional datasets. We overcome this challenge by taking two different approaches. The first approach is an unsupervised hybrid architecture, in which a Deep Belief Network (DBN) is used to extract generic underlying features, in combination with a 1SVM that uses the features learned by the DBN. DBNs have important advantages as feature detectors for anomaly detection, as DBNs use unlabelled data to capture higher-order correlations among features. Furthermore, using a DBN to reduce the number of irrelevant and redundant features improves the scalability of a 1SVM for use with large training datasets containing high-dimensional records. Our hybrid approach is able to generate an accurate anomaly detection model with lower computational and memory complexity compared to a 1SVM on its own. Alternatively, to overcome the shortcomings of 1SVMs in processing high-dimensional datasets, in our second approach we calculate a lower rank approximation of the optimisation problem that underlies the 1SVM training task. Instead of performing the optimisation in a high-dimensional space, the optimisation is conducted in a space of reduced dimension but on a larger neighbourhood. We leverage the theory of nonlinear random projections and propose the Reduced 1SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. The main objective of R1SVM is to replace a nonlinear machine with randomised features and a linear machine. In summary, we have proposed efficient privacy-preserving anomaly detection approaches for PSNs, and scalable data modelling approaches for high-dimensional datasets, which lower the computational and memory complexity compared to traditional anomaly detection techniques. We have shown that the proposed methods achieve higher or comparable accuracy in detecting anomalies compared to existing state-of-art techniques.