AI-Driven Anomaly Detection in Cloud Network Security

Artificial Intelligence (AI) and Machine Learning (ML) technologies boosted cloud network safety and anomaly detection. It also aims to find the best fit and most efficient ML methods to identify attacks and prevent breaches in cloud networks based on remote servers, among other services. The research done in this report is two-fold. One group detects and prevents breaches in cloud-based networks through machine learning methods, and the other, like genetic algorithms and artificial neural networks, bounces the ML model between cloud servers and data centers for up to tens of milliseconds. The report examines the supervised & unsupervised learning methods for their scalability, accuracy, and efficiency. The main findings are that AI is faster and more accurate than old methods, reducing most false alerts. Top AI-based secure prediction models can learn human behaviour based on the model inputs and forecast the likelihood of human intervention, prioritizing the most severe attacks.