This project implements an Intrusion Detection System (IDS) that utilizes advanced Machine Learning techniques to identify malicious requests within Wireshark pcap files. The primary dataset employed for training and testing the system is the UNSW-NB15 dataset, a widely recognized benchmark in network security research.
For comprehensive information about the project, its methodology, and results, refer to the documentation.pdf file. This documentation provides insights into the system's architecture, the Machine Learning algorithms employed, and a thorough analysis of its performance in detecting various types of network intrusions.
- https://link.springer.com/article/10.1007/s10586-019-03008-x
- https://mayanknauni.com/?p=4392
- https://github.com/AntoineRondelet/side-channel-exploit-https/blob/master/pyshark-doc.md
- https://www.kaggle.com/code/andira/eda-pada-unsw-nb15
- https://manpages.ubuntu.com/manpages/bionic/man1/ra.1.html
- https://www.systutorials.com/docs/linux/man/1-ra/