Deep Learning-based Framework for Threat Detection in Cloud Environments

Cloud computing has transformed data storage and service delivery but remains highly vulnerable to evolving security threats. Conventional threat detection methods such as Signature-Based Intrusion Detection System (SBIDS), Rule-Based Monitoring Framework (RBMF), and Statistical Anomaly Detection Method (SADM) rely heavily on predefined rules and fixed patterns, making them inefficient against zero-day attacks and dynamic cloud workloads. These approaches suffer from poor adaptability, high false alarm rates, and limited scalability across multi-tenant infrastructures. To overcome these drawbacks, the proposed Deep Learning-Based Threat Detection Framework (DLTDF) integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to extract deep spatial and temporal patterns from real-time cloud traffic. The DLTDF dynamically learns complex correlations between network events, enabling proactive identification of malicious behavior without manual feature engineering. Experimental evaluation demonstrates significant performance enhancement: accuracy improved by 29%, detection rate increased by 34%, false alarm rate reduced by 18%, and response latency minimized by 22% compared to conventional methods. The proposed framework establishes a robust and intelligent solution for adaptive security in modern cloud environments.