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Full-Text Articles in Databases and Information Systems
Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi
Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi
Electronic Theses and Dissertations
While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …
Predictive Handling Of Asynchronous Concept Drifts In Distributed Environments, Hock Hee Ang, Vivek Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi
Predictive Handling Of Asynchronous Concept Drifts In Distributed Environments, Hock Hee Ang, Vivek Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In a distributed computing environment, peers collaboratively learn to classify concepts of interest from each other. When external changes happen and their concepts drift, the peers should adapt to avoid increase in misclassification errors. The problem of adaptation becomes more difficult when the changes are asynchronous, i.e., when peers experience drifts at different times. We address this problem by developing an ensemble approach, PINE, that combines reactive adaptation via drift detection, and proactive handling of upcoming changes via early warning and adaptation across the peers. With empirical study on simulated and real-world data sets, we show that PINE handles asynchronous …