Network Traffic Analyzer Using Wireshark/Scapy | IJCT Volume 12 – Issue 6 | IJCT-V12I6P45
International Journal of Computer Techniques
ISSN 2394-2231
Author
Ruby Angel T G, Vinaya Sree K, Abinaya K, Keerthika Narayana Moorthy, Ranjini M, Jayapriya R
Abstract
In modern computer networks, efficient monitoring and analysis of traffic are essential for ensuring security, performance, and reliability. This paper presents a comprehensive network traffic analyzer utilizing Wireshark and Scapy to capture, inspect, and interpret real-time packet data. Wireshark provides a graphical interface for detailed protocol analysis, while Scapy enables custom packet manipulation and automation through Python scripting. The proposed system identifies network anomalies, detects suspicious activities, and assists in troubleshooting communication issues. Experimental results demonstrate the tool’s effectiveness in analyzing TCP/IP flows and detecting intrusions. This integration of open-source tools provides a flexible and cost-effective solution for network administrators and researchers. The study contributes to the field of network security and performance optimization through practical, scalable implementation.
Keywords
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Conclusion
The proposed Network Traffic Analyzer successfully integrates Wireshark’s detailed packet-level visualization with Scapy’s programmable automation, providing a robust platform for capturing, analyzing, and interpreting network traffic. This dual approach allows users to conduct both real- time monitoring and automated traffic testing, offering flexibility for various research and educational applications. The system enables a comprehensive understanding of protocol behaviors, flow patterns, and potential security threats. The Functional evaluation demonstrated that the analyzer accurately captures packets across multiple protocols, including TCP, UDP, ICMP, and HTTP. It effectively detects anomalies such as SYN floods, ping attacks, and malformed packets, while generating detailed reports for analysis. The combination of manual inspection through Wireshark and automated detection using Scapy ensures high reliability in diverse network scenarios.
Performance and load testing confirmed that the system maintains stable operation under moderate to high traffic conditions. Wireshark efficiently processes thousands of packets per second, while Scapy executes automated scripts without affecting system responsiveness. Memory and CPU usage remain within acceptable limits, making the framework suitable for continuous monitoring in research labs and small- to-medium enterprise networks. Despite its effectiveness, the system has certain limitations, particularly in extremely high-speed or encrypted networks where packet processing and payload inspection may become resource-intensive. Additionally, specialized protocol support may require further scripting and configuration. However, these limitations do not compromise the system’s usability for its intended scope, which includes research, teaching, and small-to-medium network monitoring.
References
[1]Ahmad, A., et al., “Wi-Fi 802.11 Analysis with Scapy and Wireshark,” GitHub, 2022. [Online]. Available: https://github.com/ericyoc/analyze-wifi-pcap-using-scapy- poc
[2]
Alam, M., et al., “A novel approach for graph-based real- time anomaly detection from dynamic network data using Wireshark,” EAI Transactions on Industrial Networks and Intelligent Systems, vol. 12, no. 2, 2025. [Online]. Available: https://eudl.eu/pdf/10.4108/eetinis.v12i2.7616
[3]Bhatia, S., and Gupta, R., “SCAPY: A powerful interactive packet manipulation program,” Semantics Scholar, Available: https://www.semanticscholar.org/paper/SCAPY-A-powerful- interactive-packet-manipulation-R.-R.-
R./a585a36e4e73f5ede47516304eda21c7613bac6e
[4]Fernandez, J., et al., “Network Traffic Tracer: Analyzing and Monitoring Network Traffic Using Python and Wireshark,” ResearchGate, 2025. [Online]. Available: https://www.researchgate.net/publication/391833181_Networ k_Traffic_Tracer_Analyzing_and_Monitoring_Network_Traf fic_Using_Python_and_Wireshark
[5]Holland, J., “Towards Reproducible Network Traffic
Analysis .Available: https://pschmitt.net/docs/pcapml.pdf
[6]Jeong, D. H., et al., “Interactive Web-Based Visual Analysis on Network Traffic,” MDPI, vol. 14, no. 1, 2022. [Online]. Available: https://www.mdpi.com/2078- 2489/14/1/16
[7]Kaya, M. O., et al., “A novel approach for graph-based real- time anomaly detection from dynamic network data using Wireshark,” EAI Transactions on Industrial Networks and Intelligent Systems, vol. 12, no. 2, 2025. [Online]. Available: https://eudl.eu/pdf/10.4108/eetinis.v12i2.7616 [8]Kumar, R., and Reddy, S., “Analyzing Network Performance Parameters,” arXiv, vol. 2302.03267, 2023. [Online]. Available: https://arxiv.org/pdf/2302.03267
[9]Mehta, D., Nikam, A., Sharma, S., and Walunj, V., “Network Traffic Analyzer,” JISEM Journal, vol. 12, no. 2, pp. 45–58, 2025. [Online]. Available: https://jisem- journal.com/index.php/journal/article/download/9952/4580/1 6601
[10]Odiathevar, M., et al., “Simulating Application Behavior for Network Monitoring,” arXiv, vol. 2502.01049, 2025. [Online]. Available: https://www.arxiv.org/pdf/2502.01049 [11]Pöttner, W. B., and Wolf, L., “IEEE 802.15.4 packet analysis with Wireshark and off-the-shelf hardware,” INSS, 2010. [Online]. Available: https://www.ibr.cs.tu- bs.de/papers/poettner-inss2010-sniffer.pdf [12]R. Tuli, “Analyzing Network Performance Parameters,” arXiv, vol. 2302.03267, 2023. [Online]. Available: https://arxiv.org/pdf/2302.03267
[13]Vishrutha, V., and G. S. Nagaraja, “Real-Time Intrusion Detection System Using Scapy with Hybrid Machine and Deep Learning Models and Smart Email Alerting,” SSRN, 2025. [Online]. Available:
https://papers.ssrn.com/sol3/Delivery.cfm/5349786.pdf?abstr actid=5349786&mirid=1 [14]Wang, W., et al., “A Real Network Environment Dataset for Traffic Analysis,” PMC, 2025. [Online]. Available:https://pmc.ncbi.nlm.nih.gov/articles/PMC120591 [15]Sikos, L. F., “Packet analysis for network forensics: A comprehensive survey,” ScienceDirect, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1742287 619302002
How to Cite This Paper
Ruby Angel T G, Vinaya Sree K, Abinaya K, Keerthika Narayana Moorthy, Ranjini M, Jayapriya R (2025). Network Traffic Analyzer Using Wireshark/Scapy. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
