AI-Driven Threat Detection: Implementing Multi-Layer Security Networks in Cloud Environments

AI-driven threat detection has become a vital tool for security maintenance as cloud environments become more and more integrated into digital infrastructures. In order to identify and reduce risks in cloud-based systems, this study investigates the deployment of multi-layer security networks utilising cutting-edge machine learning (ML) and deep learning (DL) models. We created a hybrid training strategy that blends historical and synthetic data for improved relevance in contemporary cloud environments by utilising datasets like KDD Cup 99 and CSECIC-IDS2018 in addition to simulated cloud network interactions. ML models such as Random Forest and Logistic Regression were used to create baseline threat detection, while Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks were used to accomplish deeper pattern recognition. The effectiveness of DL models in proactively protecting cloud infrastructures against changing cyber threats was demonstrated by thorough performance evaluations based on accuracy, precision, recall, F1 Score, and AUC-ROC. These evaluations also demonstrated the models’ better flexibility in dynamic cloud environments.