Detecting Cybersecurity Events from Noisy Short Text

Understand

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.