The DOI matches its Crossref record; 29 of the 31 references it cites were found in a registry or an index.
Cyberthreat Detection from Twitter using Deep Neural Networks
Open accessAbstract
To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.
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“Studies justify these features per QA1 for capturing subtle behavioral cues, such as sentiment shifts or entity relationships, that traditional metrics miss [17,70].”
From A systematic review on insider threat detection using natural language processing (2025) · cites this paper as [70]
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Metadata
- Authors
- Dionisio, N., Alves, F., Ferreira, P. M., & Bessani, A.
- Affiliations
- University of Lisbon (PT)
- Venue
- 2019 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 1-8
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2934951104
- Cited by
- 126
- References
- 31
- Citations by year
- 2026: 6 · 2025: 23 · 2024: 17 · 2023: 28 · 2022: 18 · 2021: 17 · 2020: 13 · 2019: 4
- FWCI
- 11.23
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 Framework Programme (700692)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- Twitter, open source intelligence, named entity recognition, bidirectional LSTM, convolutional neural networks, security alerts, indicators of compromise, IT infrastructure monitoring, deep neural network
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