INTRUSION DETECTION SYSTEM USING MACHINE LEARNING

Abstract

An Intrusion Detection System (IDS) is a software-based solution designed to monitor network or system
activities and identify any malicious or unauthorized actions. As networks continue to evolve, they face
increasingly sophisticated threats, making it essential to adopt advanced strategies to prevent and manage such
risks. The primary function of an IDS is to protect system resources by detecting potential attacks and alerting
administrators in a timely manner. Various techniques, methodologies, and algorithms have been developed in
the field of intrusion detection to recognize a wide range of cyberattacks. This paper aims to present a
comprehensive system that utilizes machine learning techniques to detect intrusions effectively. By learning
from previously identified attack patterns, the system is capable of recognizing both known and unknown
threats. The study further details the preprocessing steps required to prepare data for analysis, compares
different machine learning models for both training and testing phases, and discusses evaluation methods used
to measure system performance. The proposed approach highlights the importance of intelligent detection
mechanisms in enhancing network security and ensuring proactive defense against emerging cyber threats.