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 | A Privacy Enhanced Botnet Detection |
|---|---|
| ABSTRACT | Botnets remain one of the most severe cybersecurity threats, enabling large-scale attacks such as Distributed Denial of Service (DDoS), phishing, and malware propagation. Traditional botnet detection approaches rely heavily on centralized network traffic analysis, which often exposes sensitive user information and raises significant privacy concerns. To address this issue, this paper proposes a privacy-enhanced botnet detection framework that integrates deep learning with federated learning. The framework extracts meaningful behavioral patterns from network traffic while preventing the leakage of private user data. A mutual information-based privacy adversarial mechanism is incorporated to minimize private information inference while maintaining high detection accuracy. Experimental objectives focus on achieving accurate botnet detection, enhanced privacy preservation, and scalable distributed model training. The proposed approach aims to provide a secure and efficient solution against modern and sophisticated botnet attacks. |
| AUTHOR | PALLAVI Y, RAMYA M P, ACHINTYA R KRISHNA, HAMSA H V, HARSHITHA R, NANDINI P M Department of Computer Science and Engineering, Maharaja Institute of Technology Mysore Affiliated to Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312153 |
| pdf/153_A Privacy Enhanced Botnet Detection.pdf | |
| KEYWORDS | |
| References | Guangli Wu, Xingyue Wang, “A Privacy-enhanced Framework with Deep Learning for Botnet Detection,” Cybersecurity, 2025. [2] Al-Mashhadi S., Anbar M., Hasbullah I., “Hybrid Rule-Based Botnet Detection Using Machine Learning,” PeerJ Computer Science, 2021. [3] Alieyan K., Almomani A., Manasrah A., “A Survey of Botnet Detection Based on DNS,” Neural Computing and Applications, 2017. [4] Antonakakis M. et al., “Understanding the Mirai Botnet,” USENIX Security Symposium, 2017. |
