Cyber Threat Detection on Twitter Using Deep Learning Techniques: IDCNN and BiLSTM Integration
Paper
Paper/Presentation Title | Cyber Threat Detection on Twitter Using Deep Learning Techniques: IDCNN and BiLSTM Integration |
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Presentation Type | Paper |
Authors | Alsodi, Omar, Zhou, Xujuan, Gururajan, Raj, Shrestha, Anup and Btoush, Eyad |
Journal or Proceedings Title | Proceedings of 2024 Twelfth International Conference on Advanced Cloud and Big Data (CBD) |
Journal Citation | pp. 375-379 |
Number of Pages | 5 |
Year | 2025 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | Australia |
ISBN | 9798331511074 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CBD65573.2024.00073 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10858790 |
Web Address (URL) of Conference Proceedings | https://www.computer.org/csdl/proceedings/cbd/2024/245vJwagM9O |
Conference/Event | 2024 Twelfth International Conference on Advanced Cloud and Big Data (CBD) |
Event Details | 2024 Twelfth International Conference on Advanced Cloud and Big Data (CBD) Parent International Conference on Advanced Cloud and Big Data Delivery In person Event Date 28 Nov 2024 to end of 02 Dec 2024 Event Location Brisbane, Australia |
Abstract | The escalating frequency and sophistication of cyberattacks underscore the urgent need for robust threat intelligence. This paper proposes a novel approach to harnessing the wealth of information on Twitter for timely cyber threat detection. By leveraging natural language processing and Deep learning, specifically Iterated Dilated Convolutional Neural Networks (IDCNN) and Bidirectional Long Short-Term Memory (BiLSTM), we developed a IDCNN-BiLSTM learning model capable of accurately identifying cyber threats from Twitter data. Our model was trained on a comprehensive dataset of threat-related tweets and demonstrated superior performance compared to existing methods. This research contributes to the development of advanced cyber threat intelligence systems by providing a scalable and effective solution for real-time threat detection. |
Keywords | Cyber security; Deep learning; Twitter; Cybersecurity threats; social media; Iterated Dilated Convolutional Neural Networks (IDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460206. Knowledge representation and reasoning |
460403. Data security and protection | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Business |
https://research.usq.edu.au/item/zw8x5/cyber-threat-detection-on-twitter-using-deep-learning-techniques-idcnn-and-bilstm-integration
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