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

his 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.
The use of ensemble models has rendered the IDS more resilient, adaptable to diverse attack patterns, and robust against the inherent noise in network data. These findings contribute significantly to the field of cybersecurity, shedding light on the potential of uniting feature selection and ensemble models to optimise IDS performance. The practical implications of this research extend to organisations and institutions seeking to bolster their network security posture. Elevating IDS accuracy and efficiency is a pivotal step towards safeguarding networks against the continually evolving landscape of cyber threats.