AI-Driven Intrusion Detection Models for Enhancing Cloud Network Security Using Deep Learning Techniques

Abstract

Cloud computing continues to dominate modern information systems due to its scalability, flexibility, and economic advantages. However, the distributed nature of cloud infrastructures significantly expands the attack surface, introducing new and sophisticated intrusion vectors. Traditional intrusion detection systems (IDS) struggle to address these challenges because of limited adaptability, high false-positive rates, and reduced effectiveness against zero-day exploits. This research proposes an AI-driven intrusion detection framework integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks to analyze high- dimensional cloud network traffic. The hybrid deep learning architecture extracts spatial packet features and temporal behavioral patterns, enabling robust anomaly detection. Benchmark datasets such as NSL-KDD and UNSW-NB15 are utilized for training, testing, and validation. Experimental results demonstrate improved detection accuracy, precision, recall, and reduced false alarm rates when compared with conventional machine learning approaches. The proposed IDS architecture exhibits scalability, automatic feature learning, and adaptability against evolving cyber-attacks. Additionally, its modular design enables seamless integration into cloud orchestration and security management platforms. This work contributes to enhanced threat intelligence, real-time monitoring, and secure cloud service delivery, establishing a foundation for automated, self-learning cybersecurity defenses in future cloud ecosystems.