AN INTRUSION DETECTION SYSTEM WITH FEATURE SELECTION AND ENSEMBLE MACHINE LEARNING MODELS

ABSTRACT

This project delves into the realm of network security through the development and evaluation of an Intrusion Detection System (IDS) that harnesses the power of feature selection and ensemble models. In today's digitally interconnected world, the protection of networks against malicious activities and cyber threats is of paramount importance. IDSs serve as the first line of defence in identifying and mitigating these threats, making their enhancement a critical area of research. This study demonstrates the efficacy of combining feature selection methods with ensemble models to fortify IDS capabilities. By conducting an extensive review of existing IDS methodologies, collecting network data, and employing ensemble techniques, this research showcases that this approach surpasses traditional feature selection methods not only in accuracy but also in computational efficiency