Kernel methods for time series data

Kernel methods are powerful learning techniques with excellent generalization capability. This thesis develops three advanced approaches within the generic SVM framework in the application domain of time series data. The first contribution presents a new methodology for incorporating privileged information about the future evolution of time series, which is only available in the training phase. The task is prediction of the ordered categories of future time series movements. This is implemented by directly extending support vector ordinal regression with implicit constraints to leaning using privileged information paradigm. The second contribution demonstrates a novel methodology of constructing efficient kernels for time series classification problems. These kernels are constructed by representing each time series through a linear readout model from a high dimensional state space model with a fixed deterministically constructed dynamic part. Learning is then performed in the linear readout model space. Finally, in the same context, we introduce yet another novel time series kernel by co-learning the dynamic part and a global metric in the linear readout model space, encouraging time series from the same class to be represented by close model representations, while model representations of time series from different classes to be well-separated

User-profile-based analytics for detecting cloud security breaches

While the growth of cloud-based technologies has benefited the society tremendously, it has also increased the surface area for cyber attacks. Given that cloud services are prevalent today, it is critical to devise systems that detect intrusions. One form of security breach in the cloud is when cyber-criminals compromise Virtual Machines (VMs) of unwitting users and, then, utilize user resources to run time-consuming, malicious, or illegal applications for their own benefit. This work proposes a method to detect unusual resource usage trends and alert the user and the administrator in real time. We experiment with three categories of methods: simple statistical techniques, unsupervised classification, and regression. So far, our approach successfully detects anomalous resource usage when experimenting with typical trends synthesized from published real-world web server logs and cluster traces. We observe the best results with unsupervised classification, which gives an average F1-score of 0.83 for web server logs and 0.95 for the cluster traces

Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations