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