Detecting Cybersecurity Events from Noisy Short Text
Abstract
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.
Cite
@article{arxiv.1904.05054,
title = {Detecting Cybersecurity Events from Noisy Short Text},
author = {Semih Yagcioglu and Mehmet Saygin Seyfioglu and Begum Citamak and Batuhan Bardak and Seren Guldamlasioglu and Azmi Yuksel and Emin Islam Tatli},
journal= {arXiv preprint arXiv:1904.05054},
year = {2019}
}
Comments
Accepted February 2019 to North American Chapter of the Association for Computational Linguistics (NAACL) 2019