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 semantic and structural features per QA1 for capturing subtle behavioral cues that traditional metrics miss.
“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]
The Named entity recognition section (page unavailable) describes semantic word representations and extracting security-related entities from tweets, supporting the use of semantic features to identify entities. It does not show that these features capture behavioral cues such as sentiment shifts or relationships between entities, or compare them with traditional metrics on that point. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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