Towards Effective Abnormality Detection in Network Traffic: A Manual Approach Using Statistical Analysis

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Revanth Sunkara, Naga Sasank Kalapala, Jetreya Yedavalli, Atchyuth Kumar Panidepu, K.V.D. Kiran, Venkata Vara Prasad Padyala

Abstract

In an increasingly interconnected world, effective network monitoring and management are crucial for ensuring the security and performance of computer networks. This study introduces a comprehensive methodology leveraging Python programming language to enhance network device analysis and monitoring. The primary objective of this research is to develop a robust framework for automating device discovery, port scanning, real-time traffic analysis, and anomaly detection within computer networks using Python libraries and tools. We employed Python libraries such as Scapy, Nmap, Pyshark, and pandas to implement our methodology. Through empirical evaluations and practical implementations, we tested the effectiveness and scalability of our approach in enhancing network security and performance. Our research demonstrates the effectiveness of the proposed methodology in providing real-time insights into device status, behaviour, and security posture. We have shown how our approach can facilitate informed decision-making and rapid response to emerging threats, ultimately improving network resilience and security. The findings of this study highlight the potential of Python-based automation and analysis tools in network monitoring and management. While further research is needed to explore additional functionalities and validate performance in diverse network environments, our work represents a significant step forward in addressing the evolving challenges of network management and security.

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