Hybrid Deep Learning Framework for Adaptive Cybersecurity Threat Detection in Cloud Environments

Abstract

Cloud environments have become critical infrastructures due to their scalable storage and computation capabilities, attracting widespread adoption across diverse sectors. However, this popularity has also made them attractive targets for sophisticated cyber threats. The dynamic and virtualized nature of cloud computing presents unique challenges for conventional cybersecurity mechanisms. Traditional security solutions struggle to keep pace with the scale, speed, and evolving nature of cloud threats. To address these challenges, this paper proposes a hybrid deep learning framework for adaptive cybersecurity threat detection in cloud environments. The framework integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to leverage both spatial and temporal characteristics of cloud traffic data.