International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automated Cyber Threat Intelligence using Artificial Intelligence |
|---|---|
| ABSTRACT | The rapid expansion of digital systems has significantly increased exposure to cyber threats. Conventional security mechanisms often struggle to detect modern, sophisticated attacks in a timely manner. This research proposes an automated Cyber Threat Intelligence (CTI) system that leverages Artificial Intelligence (AI) to enhance threat detection, analysis, and response. The suggested framework integrates machine learning, deep learning, and natural language processing techniques to support the complete lifecycle of threat intelligence—from data collection to automated mitigation. Convolutional Neural Networks are used to identify patterns in network traffic, Long Short-Term Memory models help detect abnormal behavioral sequences, and transformer-based models analyze unstructured threat information. Experimental evaluation conducted on widely accepted cybersecurity datasets demonstrates strong performance, achieving detection accuracy close to 97% while improving precision and recall compared to traditional models. The study also considers scalability and real-time deployment challenges in enterprise environments. Overall, the research highlights the potential of AI-driven automation in strengthening cybersecurity defenses and minimizing response time. |
| TITLE | |
| AUTHOR | SUJATA SACHIN ALBHAR Assistant Professor, MCA, Siddhant Institute of Computer Application, Sudumbare Pune, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401086 |
| pdf/86_Automated Cyber Threat Intelligence.pdf | |
| KEYWORDS | |
| References | 1] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. [2] M. Conti, T. D. Nguyen, and B. Crispo, "Cyber threat intelligence," IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2921-2956, 2018. [3] NIST, "Guide to Cyber Threat Intelligence," Special Publication 800-150, 2022. [4] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterization," in Proc. 4th Int. Conf. Inf. Syst. Secur. Privacy, 2018, pp. 108-116. [5] N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection systems," in Proc. Mil. Commun. Inf. Syst. Conf., 2015, pp. 1-6. [6] P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez, and E. Vazquez, "Anomaly-based network intrusion detection: Techniques, systems and challenges," Comput. Secur., vol. 28, no. 1-2, pp. 18-28, 2009. [7] J. Kim, S. Kim, H. Kim, and M. Kim, "CNN-based malware detection using malware images," in Proc. Int. Conf. Inf. Secur. Appl., 2016, pp. 71-76. [8] C. Yin, Y. Zhu, J. Fei, and X. He, "A deep learning approach for intrusion detection using recurrent neural networks," IEEE Access, vol. 5, pp. 21954-21961, 2017. [9] G. Husari, E. Al-Shaer, M. Ahmed, B. Chu, and X. Niu, "TTPDrill: Automatic and accurate extraction of threat actions from unstructured text of CTI sources," in Proc. 33rd Annu. Comput. Secur. Appl. Conf., 2018, pp. 103-115. [10] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, "Federated learning: Challenges, methods, and future directions," IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50-60, 2020. |
