Detecting Cybersecurity Events from Noisy Short Text
This work addresses cybersecurity threat detection from social media for intelligence and crime prevention, but it is incremental as it builds on existing neural network methods.
The paper tackles the problem of detecting cybersecurity events from noisy short text, such as tweets, by proposing a model that combines domain-specific word embeddings and task-specific features, and it reports that the model outperforms traditional and neural baselines on a manually annotated dataset of 2K tweets.
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.