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 | Cyber Threat Intelligence Using Honeypots |
|---|---|
| ABSTRACT | The frequency, scope, and sophistication of cyberattacks have significantly increased as a result of the quick development of digital infrastructure and internet-connected systems. Firewalls, antivirus programs, and signature-based intrusion detection systems are examples of traditional security measures that are frequently reactive in nature and have trouble identifying developing malware variants, zero-day assaults, and advanced persistent threats (APTs). As a proactive cybersecurity strategy, Cyber Threat Intelligence (CTI) focuses on gathering, evaluating, and contextualizing threat-related data to facilitate timely and well-informed defense decisions. In this regard, honeypots which purposefully draw malevolent individuals to monitored, controlled environments have drawn a lot of attention as useful instruments for obtaining high-fidelity threat intelligence. The study emphasizes how useful honeypots are for identifying persistent threat sources, comprehending attack lifecycles, and assisting with well-informed security decision-making. The findings demonstrate that incorporating honeypots into a cyber threat intelligence framework improves organizational security posture and permits a proactive, intelligence-driven approach to identifying, evaluating and mitigating cyber threats, despite difficulties with secure deployment, scalability and legal considerations. |
| AUTHOR | AISHWARYA S KANCHAN, ANUPAMA C Y, APOORVA K M, VISHNU MAHENDRA REDDY L, DR. LATHA B M UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Head of the Department, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312129 |
| pdf/129_Cyber Threat Intelligence Using Honeypots.pdf | |
| KEYWORDS | |
| References | 1. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767,Apr.2018. [Online]. Available: https://arxiv.org/abs/1804.02767 2. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767 ,Apr.2018.[Online]. Available: https://arxiv.org/abs/1804.02767 3. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of ObjectDetection,”arXivpreprintarXiv:2004.10934,Apr.2020. [Online]. Available: https://arxiv.org/abs/2004.10934 4. C.-Y. Wang, H.-Y. M. Liao, Y.-H. Wu, P.-Y.Chen, J.-W. Hsieh, and I.-H. Yeh, “CSPNet: A New Backbone That Can Enhance Learning Capability of CNN,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA,2020,pp.390–391,doi: 10.1109/CVPRW50498.2020.00203. 5. A. Bochkovskiy, C.-Y. Wang, H.-Y. M. Liao, and P.-Y. Chen, “Scaled-YOLOv4: Scaling Cross Stage Partial Network,” arXiv preprint arXiv:2011.08036,Nov.2020.[Online]. Available: https://arxiv.org/abs/2011.08036 6. H. Tang, Z. Yang, Y. Zhang, and M. Zhou, “An Improved YOLOv3 Model for Real-Time Multi-Scale Object Detection,” in 2020 IEEE International Conference on Computer Vision Workshops (ICCVW), Seoul, Korea, 2020, pp. 386–393. 7. H. Xu, H. Wang, and Y. Guo, “Real-Time Object Detection in Autonomous Driving Using YOLOv3 Model,” in 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China, 2019, pp. 1874–1878, doi: 10.1109/ICMA.2019.8816429. |
