Network Traffic Analysis Using Sniffer

Abstract

The growing sophistication of digital networks has been matched by the proliferation of cyber threats, from email and global ransomware phishing to identity theft or fraud or more sophisticated denial-of-service attacks. Although it helps in a lot of cases, traditional network monitoring systems are still expensive and resource intensive, leaving a void that needs to be filled with a lightweight yet strong solution. This challenge has propelled the creation of tools which can capture network traffic in real-time, perform deep-packet analysis, and detect anomalies quickly. Experimental study shows that the tool can accurately capture and analyze live traffic streams, detect anomalous patterns of activity, and prevent fully detailed logs for forensic analysis. Performance testing ensures the system works well with multiple network traffic loads offering proper output without big latencies and packet loss.

Introduction

The rapid growth of digital networks has led to increased exposure to cyber threats such as:

• Data breaches

• Malware

• Unauthorized access

• DDoS attacks

With network infrastructure critical to modern organizations, even minor disruptions can lead to major losses. However, existing network monitoring tools are often:

• Expensive

• Resource-heavy

• Lacking scalability

• Not suited for small or specialized environments

???? Proposed Solution:
A Python-based, lightweight, cross-platform Network Mapping Tool for:

• Real-time packet capture

• Protocol decoding

• Anomaly detection

• Structured logging for compliance and forensics

Built with a modular architecture, it supports both Windows and Linux, ensuring easy integration and performance monitoring without system overload.

II. Literature Survey

Past research offers various solutions but with limitations:

• Low-level packet sniffing: High precision, but hardware-dependent and not scalable

• Signature-based IDS: Good for known threats but ineffective for new/zero-day attacks

• ML-based anomaly detection: Powerful but requires heavy computation and training data

• Modular frameworks: Easy to maintain but lack detailed reporting and compliance features

???? Conclusion: There is a gap in the market for a lightweight, flexible, forensic-capable tool. The proposed system bridges this gap by integrating the strengths of existing methods while remaining efficient and platform-independent.

III. Methodology

The system follows a 4-phase approach:

1. Data Acquisition

• Captures raw packets using Scapy and Socket in Python

• Extracts metadata: IPs, ports, protocol type, size, and timestamp

2. Data Processing & Filtering

• Rule-based filtering to:

  • Exclude safe/known traffic

  • Focus on critical ports/protocols

  • Detect abnormal patterns

• Reduces system load and speeds up analysis

3. Anomaly Detection

• Currently rule-based

  • Detects traffic spikes, repeated failed connections, abnormal hours, etc.

• Future integration of machine learning planned for adaptive detection

4. Logging & Reporting

• Structured log files with metadata for each packet

• Supports:

  • Forensics

  • Compliance auditing

  • Historical analysis

• Exports in TXT, CSV with visual summaries for managers

IV. Results

The tool was tested extensively for performance and reliability across metrics:

???? These results confirm that the tool:

• Is accurate and lightweight

• Works in real time

• Supports cross-platform environments

• Is ideal for both large-scale and resource-constrained deployments

? Key Features

• Real-time packet capture and filtering

• Modular and scalable design

• Forensic-ready structured logs

• Supports compliance and auditing

• Cross-platform (Windows + Linux)

• Low resource footprint

• Future-ready for machine learning integration

Conclusion

The problem statement provided above was successfully solved and the efficient, accurate, resource-friendly framework that captures, analyzes, and visualizes network traffic was implemented as a Network Mapping Tool for Desktop. This allows for full monitoring with minimal latency and great reliability while ensuring that network administrators and security analysts can capture a packet using the same flow hep, filter packets by aforementioned fields, it identify its protocol, detect anomalies in real time and visualize them via dashboards. Validation results demonstrate the effectiveness of the framework, with a 98% packet capturing accuracy and 97% protocol identification precision under realistic networks. In addition to the low CPU utilization of 15% and improved memory usage of 250 MB these findings show that the system should be able to run without taxing system resources. Together, these metrics support the capability of the framework to be deployed not only in research environment but also for enterprise. This approach emerges as a solid candidate to improve network security, for the precise protocol analysis, anomaly detection and extensive reporting are fused into one tool. It does not just satisfy the problem statement, but in fact it makes the problem more powerful by providing scalable high accurate, and low resource consuming services to solve. In the future, this solution can supported by integrating machine learning based predictive analytics for threat detection as well a more cloud packet capture and automated incident response workflows. Further enhancing it to support distributed monitoring across multiple nodes would better scale the solution and provide adaptivity in large-scale network scenarios.

References

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Copyright

Copyright © 2025 Miss. Shreya K, Dr. SDN Hayath Ali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.