A Machine Learning Framework with Optimizations Towards an Efficient Intrusion Detection System

Abstract

With the escalating number of attacks on information systems and networks, the need for robust cybersecurity measures has never been more pressing. Leveraging the power of Artificial Intelligence (AI) and Machine Learning (ML), we can develop intrusion detection systems that are more adaptive and efficient. While existing machine learning models can be used for intrusion detection, their performance can be significantly improved through hyperparameter tuning and feature engineering. This paper introduces a machine learning-based framework that is specifically designed to create an efficient intrusion detection system. The framework incorporates a hybrid feature engineering methodology and hyperparameter tuning using Bayesian Optimization for machine learning models. We propose an ensemble of machine-learning models that have been fine-tuned for superior intrusion detection performance. Our proposed algorithm, known as Ensemble Learning-Based Intrusion Detection (ELBID), harnesses the power of machine learning models with optimizations for intelligent detection of intrusions. The ensemble model we have developed surpasses many existing ML models in intrusion detection, achieving an impressive accuracy of 94.59%. As a result, our optimized ensemble model can be seamlessly integrated into real-world applications, significantly enhancing cyber security.