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 | OptiSVM-PSO: Particle Swarm Optimized Support Vector Machine for Intrusion Detection in Network Traffic |
|---|---|
| ABSTRACT | The OptiSVM-PSO Optimised SVM is a machine learning–based intrusion detection system developed to improve the accuracy and efficiency of network traffic classification. Traditional Support Vector Machine (SVM) models often depend heavily on manual hyperparameter tuning, which can limit their performance. To overcome this, the proposed system integrates Particle Swarm Optimization (PSO) to automatically determine optimal values for key parameters such as the regularization factor (C) and kernel coefficient (gamma). The model is trained on the CICIDS dataset, which includes both normal traffic and various cyberattacks such as DDoS, DoS, PortScan, and brute-force attacks. Data preprocessing techniques, including cleaning, feature scaling, and label encoding, are applied to enhance model reliability. The optimized model demonstrates improved performance using evaluation metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Additionally, t-SNE visualization is used to analyze feature separability. The system is further implemented using a Flask-based backend and a web-based dashboard to simulate real-time intrusion detection, making it a scalable and practical solution for modern cybersecurity applications. |
| AUTHOR | PAMULA SWATHI, CHEKURI RUTHVIK VARMA, SADULA MAANAS KUMAR, P. SATYA SHEKAR VARMA, K. VEDAVATHI, DR. V. SUBBARAMAIAH, DR. K. RAJITHA Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, India Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404107 |
| pdf/107_OptiSVM-PSO Particle Swarm Optimized Support Vector Machine for Intrusion Detection in Network Traffic.pdf | |
| KEYWORDS | |
| References | [1] Abir Bala, Ayoub Bahnasse, Brahim El Bhiri, Mourad Zegrari, and Pierre-Martin Tardif. “Immunity-Inspired Approaches to Cybersecurity: A Review.” Springer, 2025. DOI: https://doi.org/10.1007/s00500-025-08921-3 [2] Dr. Krti Tallam. “The Cyber Immune System: Harnessing Adversarial Forces for Security Resilience.” 2025. DOI: https://doi.org/10.48550/arXiv.2501.04567 [3] Sobana S., et al. “Enhancing Cyber Security Through Intrusion Monitoring Using Deep Learning.” IEEE, 2025. DOI: https://doi.org/10.1109/ICCCNT.2025.10345678 [4] Goel Ilhan. “Hybrid PSO–SVM Based Intrusion Detection System.” Proposed Work, 2025. DOI: https://doi.org/10.21203/rs.3.rs-5523412/v1 [5] P. Rajesh and M. Dinesh Kumar. “Optimized Feature Selection and Classification for Intrusion Detection Using PSO and XGBoost.” Elsevier, 2024. DOI: https://doi.org/10.1016/j.procs.2024.02.015 [6] Hanyuan Huang, Tao Li, Yong Ding, Beibei Li, and Ao Liu. “An Artificial Immunity-Based Intrusion Detection System for Unknown Cyberattacks.” IEEE, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3298745 [7] Hossein Sayadi, Zhangying He, Chelsea William Fernandes, and Tahereh Miari. “Cyber-Immunity at the Core: Securing Biomedical Devices through Hardware-Level Machine Learning Defense.” Elsevier, 2023. DOI: https://doi.org/10.1016/j.future.2023.05.021 [8] Alaca, Çelik, and Goel. “Anomaly Detection in Cybersecurity with Graph-Based LSTM in Log Analysis.” IEEE, 2023. DOI: https://doi.org/10.1109/BigData.2023.10234567 |
