Project Abstract

This report details a Python-based tool designed to strengthen network security through proactive threat detection. The script focuses on identifying potential network sniffers, malicious devices that passively monitor network traffic to intercept and steal sensitive data such as passwords, usernames, and other confidential information. By carefully analysing network traffic patterns, the script pinpoints a key indicator of sniffer activity: the presence of duplicate MAC addresses. This proactive monitoring approach provides an additional layer of defence, safeguarding your network against unauthorized access. The script offers a valuable resource for IT administrators seeking to enhance security and protect the confidentiality of information within their network environment. In summary, this study advances a proactive strategy for network security and gives IT managers a useful tool to effectively counteract changing cyberthreats. It deepens our understanding of proactive threat detection strategies and provides workable solutions to strengthen network defenses in the face of an increasingly perilous digital environment.

Keywords: Cyber Security, Network Traffic Analysis, Machine Learning

 Conference Details

Session: Presentation Stream 28 at Presentation Slot 5

Location: GH001 at Wednesday 8th 13:30 – 17:00

Markers: Neil Carter, Gary Tam

Course: MSc Cyber Security, Masters PG

Future Plans: I’m looking for an industry placement