@inproceedings{yagcioglu-etal-2019-detecting,
title = "Detecting Cybersecurity Events from Noisy Short Text",
author = "Yagcioglu, Semih and
Seyfioglu, Mehmet Saygin and
Citamak, Begum and
Bardak, Batuhan and
Guldamlasioglu, Seren and
Yuksel, Azmi and
Tatli, Emin Islam",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1138",
doi = "10.18653/v1/N19-1138",
pages = "1366--1372",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting Cybersecurity Events from Noisy Short Text
%A Yagcioglu, Semih
%A Seyfioglu, Mehmet Saygin
%A Citamak, Begum
%A Bardak, Batuhan
%A Guldamlasioglu, Seren
%A Yuksel, Azmi
%A Tatli, Emin Islam
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F yagcioglu-etal-2019-detecting
%X 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.
%R 10.18653/v1/N19-1138
%U https://aclanthology.org/N19-1138
%U https://doi.org/10.18653/v1/N19-1138
%P 1366-1372
Markdown (Informal)
[Detecting Cybersecurity Events from Noisy Short Text](https://aclanthology.org/N19-1138) (Yagcioglu et al., NAACL 2019)
ACL
- Semih Yagcioglu, Mehmet Saygin Seyfioglu, Begum Citamak, Batuhan Bardak, Seren Guldamlasioglu, Azmi Yuksel, and Emin Islam Tatli. 2019. Detecting Cybersecurity Events from Noisy Short Text. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1366–1372, Minneapolis, Minnesota. Association for Computational Linguistics.