Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms

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
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