Cyber Threat Detection on Twitter Using Deep Learning Techniques: IDCNN and BiLSTM Integration

• Published in: International Conference on Advanced Cloud and Big Data pp. 375 - 379
• Main Authors: Alsodi, Omar, Zhou, Xujuan, Gururajan, Raj, Shrestha, Anup, Btoush, Eyad
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.11.2024
• Subjects: Accuracy; Bidirectional long short term memory; Bidirectional Long Short-Term Memory (BiLSTM); Blogs; Convolutional neural networks; Cyber security; Cybersecurity threats; Data models; Deep learning; Iterated Dilated Convolutional Neural Networks (IDCNN); Natural language processing; Real-time systems; social media; Social networking (online); Threat assessment; Twitter
• ISSN: 2573-301X
• DOI: 10.1109/CBD65573.2024.00073

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.