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New advances in genomic technology make it possible to address some of the most fundamental questions in biology for the first time. They also highlight a need for new approaches to analyze and model massive amounts of complex data. In this thesis, I present six research projects that illustrate the exciting interaction between high-throughput genomic experiments, new machine learning algorithms, and mathematical modeling. This interdisci- plinary approach gives insights into questions ranging from how variations in the epigenome lead to diseases across human populations to how the slime mold finds the shortest path. The algorithms and models developed here are also of interest to the broader machine learning community, and have applications in other domains such as text modeling. / Mathematics

Underwater video data are a rich source of information for marine biologists. However, the large amount of recorded video creates a ’big data’ problem, which emphasizes the need for automated detection techniques. This work focuses on the detection of quasi-stationary crabs of various sizes in deep-sea images. Specific issues related to image quality such as low contrast and non-uniform lighting are addressed by the pre-processing step. The segmentation step is based on color, size and shape considerations. Segmentation identifies regions that potentially correspond to crabs. These regions are normalized to be invariant to scale and translation. Feature vectors are formed by the normalized regions, and they are further classified via supervised and non-supervised machine learning techniques. The proposed approach is evaluated experimentally using a video dataset available from Ocean Networks Canada. The thesis provides an in-depth discussion about the performance of the proposed algorithms. / Graduate / 0544 / 0800 / 0547 / mars_mehr@hotmail.com

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published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

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The intrusion detection in computer networks is a complex research problem, which requires the understanding of computer networks and the mechanism of intrusions, the configuration of sensors and the collected data, the selection of the relevant attributes, and the monitor algorithms for online detection. It is critical to develop general methods for data dimension reduction, effective monitoring algorithms for intrusion detection, and means for their performance improvement. This dissertation is motivated by the timely need to develop statistics-based machine learning methods for effective detection of computer network anomalies.Three fundamental research issues related to data dimension reduction, control charts design and performance improvement have been addressed accordingly. The major research activities and corresponding contributions are summarized as follows:(1) Filter and Wrapper models are integrated to extract a small number of the informative attributes for computer network intrusion detection. A two-phase analyses method is proposed for the integration of Filter and Wrapper models. The proposed method has successfully reduced the original 41 attributes to 12 informative attributes while increasing the accuracy of the model. The comparison of the results in each phase shows the effectiveness of the proposed method.(2) Supervised kernel based control charts for anomaly intrusion detection. We propose to construct control charts in a feature space. The first contribution is the use of multi-objective Genetic Algorithm in the parameter pre-selection for SVM based control charts. The second contribution is the performance evaluation of supervised kernel based control charts.(3) Unsupervised kernel based control charts for anomaly intrusion detection. Two types of unsupervised kernel based control charts are investigated: Kernel PCA control charts and Support Vector Clustering based control charts. The applications of SVC based control charts on computer networks audit data are also discussed to demonstrate the effectiveness of the proposed method.Although the developed methodologies in this dissertation are demonstrated in the computer network intrusion detection applications, the methodologies are also expected to be applied to other complex system monitoring, where the database consists of a large dimensional data with non-Gaussian distribution.

We present a technique to identify deviating systems among a group of systems in a self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix. The compressed representation is computed with Principal Component Analysis and Kernel Principal Component Analysis. The similarity measure between two compressed representations is based on the angle between the spaces spanned by the principal components, but other methods of calculating a similarity measure are suggested as well. The subsequent deviation detection is carried out by computing the probability of each system to be observed given all the other systems. Clustering of the systems is done with hierarchical clustering and spectral clustering. The whole technique is demonstrated on four data sets of mechanical systems, two of a simulated cooling system and two of human gait. The results show its applicability on these mechanical systems.