Network Intruison Detection System

Gokul A; Mahendiran N

doi:10.71366/ijwos

Authors

Gokul A PG Student, Sri RamaKrishna College Of Arts & science
Author
Mahendiran N Associate Professor, Sri RamaKrishna College Of Arts & Science
Author

DOI:

https://doi.org/10.71366/ijwos

Keywords:

Network Intrusion Detection System (NIDS), Machine Learning, Cybersecurity, Random Forest, Decision Tree, K-Nearest Neighbors (KNN), KDD Cup 1999 Dataset, Anomaly Detection, Network Security.

Abstract

The Project entitled “NETWORK INTRUSION DETECTION SYSTEM” ensures the security and integrity of computer systems is paramount in the realm of cybersecurity. This project is build using machine learning techniques, specifically employing Random Forest, Decision Tree, and K- Nearest Neighbors classifiers. The project utilizes the KDD Cup 1999 dataset, a widely recognized benchmark dataset for intrusion detection. This project provides a foundation for the development of robust and adaptive intrusion detection systems, demonstrating the practical application of machine learning in enhancing cybersecurity measure.