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Personalized Web Search By Using Learned User Profiles In Re-Ranking, Jia Hu
Personalized Web Search By Using Learned User Profiles In Re-Ranking, Jia Hu
Theses and Dissertations
Search engines return results mainly based on the submitted query; however, the same query could be in different contexts because individual users have different interests. To improve the relevance of search results, we propose re-ranking results based on a learned user profile. In our previous work we introduced a scoring function for re-ranking search results based on a learned User Interest Hierarchy (UIH). Our results indicate that we can improve relevance at lower ranks, but not at the top 5 ranks. In this thesis, we improve the scoring function by incorporating new term characteristics, image characteristics and pivoted length normalization. …
Machine Learning For Host-Based Anomaly Detection, Gaurav Tandon
Machine Learning For Host-Based Anomaly Detection, Gaurav Tandon
Theses and Dissertations
Anomaly detection techniques complement signature based methods for intrusion detection. Machine learning approaches are applied to anomaly detection for automated learning and detection. Traditional host-based anomaly detectors model system call sequences to detect novel attacks. This dissertation makes four key contributions to detect host anomalies. First, we present an unsupervised approach to clean training data using novel representations for system call sequences. Second, supervised learning with system call arguments and other attributes is proposed for enriched modeling. Third, techniques to increase model coverage for improved accuracy are presented. Fourth, we propose spatio-temporal modeling to detect suspicious behavior for mobile hosts. …