Machine Learning for Intrusion Detection in Cloud-Based Systems

Abstract

The proliferation of cloud computing has transformed data storage and processing but also introduced complex security challenges. Traditional Intrusion Detection Systems (IDS) often struggle in dynamic cloud environments due to scalability, adaptability, and the high rate of false positives. Machine Learning (ML) has emerged as a powerful tool to enhance IDS by enabling systems to learn from vast datasets, identify anomalous behavior, and adapt to evolving threats. This paper investigates the application of ML techniques such as supervised, unsupervised, and deep learning to intrusion detection in cloud-based systems. It reviews key methodologies, evaluates performance across widely used benchmark datasets (NSL-KDD, CICIDS2017), and highlights real-world implementations in commercial cloud platforms. The study also addresses critical challenges including data privacy, adversarial ML, real-time detection, and scalability. Through a comprehensive analysis, we identify promising research directions such as federated learning, explainable AI, and hybrid cloud-edge IDS architectures.