Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms
Author(s):
S Sai Varun, D. Sai Sumanth, G. Sai Vardhan, P. Sai Vamshi, S. Sai Varshith, T. Sai Sushanth
Keywords:
Abstract
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

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews