LOW-LATENCY DEEP LEARNING–BASED INTRUSION DETECTION FOR CLOUD SECURITY

Abstract

Cloud computing has become a fundamental platform for modern digital services due to its scalability and flexibility. However, the shared and distributed nature of cloud networks exposes them to a wide range of cyber threats. Traditional intrusion detection systems struggle to handle large-scale, dynamic, and high-dimensional cloud traffic. This paper presents an intelligent intrusion detection system for cloud networks using deep learning techniques. The proposed system automatically learns discriminative features from network traffic to detect malicious activities. Deep neural networks are employed to improve detection accuracy and reduce false alarms. Extensive experiments demonstrate superior performance compared to conventional machine learning approaches. The system effectively detects both known and unknown attacks. The results confirm the suitability of deep learning for enhancing cloud network security.