Enhancing Intrusion Detection Systems with Machine Learning

Traditional Intrusion Detection Systems (IDS) often struggle to effectively address the evolving landscape of cyber threats. This research explores the potential of machine learning techniques to improve the accuracy, speed, and adaptability of IDS. This research study reviews various machine learning approaches, including classification, regression, clustering, dimensionality reduction, semi-supervised, and reinforcement learning, and their application to intrusion detection. Effective data preprocessing and feature selection techniques are crucial for optimizing the performance of machine learning models. The evaluation of IDS performance is discussed, focusing on metrics such as accuracy, precision, recall, and ROC-AUC. The challenges associated with low-quality data, computational complexity, and adversarial attacks are highlighted, along with potential future research directions. By integrating machine learning, IDS can be transformed into more intelligent and adaptive systems, capable of detecting and responding to advanced cyber threats. This research contributes to the advancement of cybersecurity by providing a comprehensive overview of machine learning techniques and their application to intrusion detection.