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 | Real-Time Network Traffic Monitoring and Anomaly Detection Dashboard |
|---|---|
| ABSTRACT | Monitoring network activity in realime has become a necessity for any organisation that takes cybersecurity seriously. This paper describes a real-time network traffic monitoring and anomaly detection dashboard built using a Node.js backend, a React-based frontend, and Supabase as the persistence and authentication layer. The system captures raw packets through node-pcap, extracting fields such as source and destination IP, transport protocol, payload size, and timestamp, then stores them in a PostgreSQL database. A rule-based engine running server-side inspects each packet against configurable thresholds, flagging high packet rates, uncommon port combinations, and DDoS-like patterns. Alerts are pushed to connected browsers in under a second via Socket.io. The frontend presents analysts with a live traffic chart, protocol breakdown, top-talker rankings, a rolling alert feed, and at-a-glance counters. Evaluation on a simulated campus network showed the system correctly flagged synthetic attack traffic while keeping false positives manageable, demonstrating that a JavaScript-first stack can deliver meaningful security visibility without dedicated SIEM infrastructure. |
| AUTHOR | D.S. DEEPIKA, KANISHIK E, BHARATHWAJ K S, HARISH B Assistant Professor, Department of Information Technology, R.M.D Engineering College, Thiruvallur,Tamil Nadu, India U.G. Student, Department of Information Technology, R.M.D Engineering College, Thiruvallur,Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403041 |
| pdf/41_Real Time Network Traffic Monitoring and Anomaly Detection Dashboard.pdf | |
| KEYWORDS | |
| References | [1] A. Lakhina, M. Crovella, and C. Diot, “Diagnosing network-wide traffic anomalies,” Proc. ACM SIGCOMM, Portland, OR, 2004, pp. 219–230. [2] M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” Proc. IEEE CISDA, Nashville, TN, 2009, pp. 1–6. [3] R. Vinayakumar, M. Alazab, and K. P. Soman, “Deep learning approach for intelligent intrusion detection system,” IEEE Access, vol. 7, pp. 41525–41550, 2019. [4] M. Roesch, “Snort: Lightweight intrusion detection for networks,” Proc. USENIX LISA, Seattle, WA, 1999, pp. 229–238. [5] M. Ahmed, A. N. Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” J. Netw. Comput. Appl., vol. 60, pp. 19–420, 2021. [6] J. Kreps, N. Narkhede, and J. Rao, “Kafka: A distributed messaging system for log processing,” Proc. NetDB Workshop, Athens, Greece, 2011, pp. 1–7. [7] T. T. Nguyen and G. Armitage, “A survey of techniques for internet traffic classification using machine learning,” IEEE Commun. Surv. Tutor., vol. 10, no. 4, pp. 56–197, 2021. [8] R. F. Erbacher, K. L. Walker, and D. A. Frincke, “Intrusion and misuse detection in large-scale systems,” IEEE Comput. Graph. Appl., vol. 22, no. 1, pp. 38–47, 2002. |
