Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms
S Sai Varun, D. Sai Sumanth, G. Sai Vardhan, P. Sai Vamshi, S. Sai Varshith, T. Sai Sushanth
This project focuses on advanced machine learning for detecting intrusions in imbalanced network traffic. Despite challenges posed by data imbalance, the study employs cutting-edge algorithms and diverse preprocessing to glean insights from normal and malicious patterns. Findings highlight machine learning's potential in managing imbalanced network traffic, emphasizing tailored preprocessing and algorithm selection. Ultimately, the project advances intrusion detection by showcasing machine learning's role in enhancing security through swift threat identification and mitigation. In conclusion, this research underscores the pivotal role of machine learning in addressing imbalanced network scenarios, paving the way for a safer digital landscape.
Article Details
Unique Paper ID: 161987

Publication Volume & Issue: Volume 10, Issue 7

Page(s): 154 - 160
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews