ABSTRACT
This chapter presents a number of approaches to detect insider threats through augmented unsupervised and supervised learning techniques on evolving stream. In spite of the success and extensive studies of stream mining techniques, there is no single work dedicated to a unified study of the new challenges introduced by big data and evolving stream data. Only a few tools support very basic stream mining. Research challenges such as change detection, novelty detection, and feature evolution over evolving streams have been recently studied in traditional stream mining, but not in big data. In addition to presenting the solutions to overcome stream mining challenges, experience with real applications of these techniques to data mining and security will be shared across the world. New classes evolving in the stream are known as concept evolution which makes classification difficult. The big data community adopts big data infrastructures that are used to process unbounded continuous streams of data.
