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We present a new machine learning and text information extraction approach todetection of cyber threat events in Twitter that are novel (previouslynon-extant) and developing (marked by significance with respect to similaritywith a previously detected event). While some existing approaches to eventdetection measure novelty and trendiness, typically as independent criteria andoccasionally as a holistic measure, this work focuses on detecting both noveland developing events using an unsupervised machine learning approach.Furthermore, our proposed approach enables the ranking of cyber threat eventsbased on an importance score by extracting the tweet terms that arecharacterized as named entities, keywords, or both. We also impute influence tousers in order to assign a weighted score to noun phrases in proportion to userinfluence and the corresponding event scores for named entities and keywords.To evaluate the performance of our proposed approach, we measure the efficiencyand detection error rate for events over a specified time interval, relative tohuman annotator ground truth.

A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams