Network security enhancement with advanced techniques in network scanning

This article details the development and evaluation of an advanced network scanning framework that uses Scapy for packet manipulation and Tkinter for the GUI. The framework aims to improve detection accuracy, resource efficiency, and user interface usability, addressing the shortcomings of traditional methods. Key components include a Network Scanner, Packet Handler, Security Measures, and a GUI, all designed for comprehensive network security. Tested in a controlled environment, the framework achieved a 100% detection rate, surpassing traditional methods, and demonstrated resource efficiency with a 20% reduction in CPU usage and a 60 MB decrease in memory usage. The analysis showed network diversity and low utilization, suggesting capacity for future expansion. The framework's GUI effectively displayed scan results, enhancing usability. The novelty of this research lies in its integration of optimized ARP packet handling with a user-friendly interface, ensuring real-time threat detection and scalability. The study contributes to the advancement of network scanning techniques and proposes future enhancements like machine learning integration for better threat detection.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-30
DOI
https://doi.org/10.5281/zenodo.14906441
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Network security enhancement with advanced techniques in network scanning

Zenodo (CERN European Organization for Nuclear Research)
Network Security and Intrusion Detection
article

Network security enhancement with advanced techniques in network scanning

article en

Abstract

This article details the development and evaluation of an advanced network scanning framework that uses Scapy for packet manipulation and Tkinter for the GUI. The framework aims to improve detection accuracy, resource efficiency, and user interface usability, addressing the shortcomings of traditional methods. Key components include a Network Scanner, Packet Handler, Security Measures, and a GUI, all designed for comprehensive network security. Tested in a controlled environment, the framework achieved a 100% detection rate, surpassing traditional methods, and demonstrated resource efficiency with a 20% reduction in CPU usage and a 60 MB decrease in memory usage. The analysis showed network diversity and low utilization, suggesting capacity for future expansion. The framework's GUI effectively displayed scan results, enhancing usability. The novelty of this research lies in its integration of optimized ARP packet handling with a user-friendly interface, ensuring real-time threat detection and scalability. The study contributes to the advancement of network scanning techniques and proposes future enhancements like machine learning integration for better threat detection.

Zenodo (CERN European Organization for Nuclear Research)
University of Sussex (GB)
Decent work and economic growth
Openalex Percentile: Top 58%
Network Security and Intrusion Detection
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Network security enhancement with advanced techniques in network scanning · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS