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 | Application Performance and ISP Fault Prediction Using Network Traffic Analysis |
|---|---|
| ABSTRACT | The performance of modern web applications depends significantly on reliable network connectivity. Users often experience issues such as high latency, packet loss, buffering, and service interruptions, making it difficult to determine whether the problem originates from Internet Service Provider (ISP) networks or application-level faults. This paper presents a machine learning-based framework for Application Performance and ISP Fault Prediction using network traffic data captured through Wireshark. Key performance metrics including latency, packet loss, throughput, jitter, retransmissions, and packet count are extracted and analyzed. Machine learning models such as Random Forest and XGBoost are employed to classify network conditions into Normal, Network Fault, and Application Fault categories. A Streamlit-based dashboard provides interactive visualization and monitoring. Experimental results demonstrate improved fault diagnosis, reduced troubleshooting time, and enhanced network reliability. |
| AUTHOR | BARIGE ABHISHEK, DR. V. UMA RANI Post Graduate Student, Department of Computer Science and Engineering, Data Science, Jawaharlal Nehru Technological University, Hyderabad, India Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407031 |
| pdf/31_Application Performance and ISP Fault Prediction Using Network Traffic Analysis.pdf | |
| KEYWORDS | |
| References | 1. Parvat, T., Kolwankar, S., Lopes, N., Sawant, P., & Parchurkar, S., “Network Traffic Analysis Using Machine Learning,” Indian Journal of Computer Science, Vol. 9, Issue 3, May–June 2024, pp. 32–41, DOI: 10.17010/ijcs/2024/v9/i3/174151. 2. Hu, X., & Zhou, Y., “Wireshark Packet Dissector for DL/T 860 Protocol,” 2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT), Shanghai, China, 2022, pp. 30–34, DOI: 10.1109/CEECT55960.2022.10030402. 3. Priya, N. S., Meyyappan, S., Balasubramanian, K. N., & Pruthiev, A. S., “Network Attack Detection Using Machine Learning,” 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2022, pp. 342–346, DOI: 10.1109/ICACCS54159.2022.9785263. 4. Chauhan, R., & Kumar, S., “Packet Loss Prediction Using Artificial Intelligence Unified with Big Data Analytics, Internet of Things and Cloud Computing Technologies,” 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2021, pp. 1–6, DOI: 10.1109/ISCON52037.2021.9702517. 5. Siswanto, A., Syukur, A., Kadir, E. A., & Suratin, “Network Traffic Monitoring and Analysis Using Packet Sniffer,” 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), Rabat, Morocco, 2019, pp. 1–4, DOI: 10.1109/COMMNET.2019.8742369. 6. Arvind, S., Silveri, V. K., Potey, G., Nunavath, P., & Podishetty, R., “Network Traffic Virtualization Using Wireshark and Google Maps,” 2023 International Conference on Distributed Computing, Electrical Circuits and Electronics (ICDCECE), India, 2023, pp. 1–6, DOI: 10.1109/ICDCECE57866.2023.10150823. 7. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, 2011, pp. 2825–2830. 8. McKinney, W., “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, 2010, pp. 56–61. 9. Hunter, J. D., “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, Vol. 9, No. 3, 2007, pp. 90–95. 10. Chen, T., & Guestrin, C., “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794 |
