ברכה שפירא

For more than a century, Markov chain models have had a tremendous effect on research and industry alike in various domains such as physics, chemistry, biology, and computer science. However, limited research has been performed on how to incorporate contextual conditions into the modeling phase. Existing approaches are performed in an ad hoc manner or fail to consider multiple contextual dimensions simultaneously. Inspired by the main paradigms in context-aware recommender systems, we suggest five novel approaches for learning contextual Markov chain models. In particular we suggest three contextual pre-filtering models that operate by learning multiple sub-models from various data partitions, each tailored to specific contextual aspects; a contextual model that directly extents the Markovian model with the contextual features; and lastly, a post-filtering model, which serves to refine the predictions made by a traditional non-contextual model by considering the available contextual information. We evaluate the suggested methods in two use cases: analysis of web browsing activities, and attack propagation patterns in honeypot systems. Experiments with massive datasets that contain millions of records indicate that taking the context of modeled sequences into consideration can substantially improve the accuracy of predicting and ranking the next possible element in such sequences. Moreover, we demonstrate the high scalability of the suggested methods, making them suitable for analyzing a vast amount of sequential data.