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It is very critical to analyze messages shared over social networks for cyberthreat intelligence and cyber-crime prevention. In this study, we propose amethod that leverages both domain-specific word embeddings and task-specificfeatures to detect cyber security events from tweets. Our model employs aconvolutional neural network (CNN) and a long short-term memory (LSTM)recurrent neural network which takes word level meta-embeddings as inputs andincorporates contextual embeddings to classify noisy short text. We collected anew dataset of cyber security related tweets from Twitter and manuallyannotated a subset of 2K of them. We experimented with this dataset andconcluded that the proposed model outperforms both traditional and neuralbaselines. The results suggest that our method works well for detecting cybersecurity events from noisy short text.

Detecting Cybersecurity Events from Noisy Short Text