AI-Based Intrusion Detection and Security Threat Analysis in Cloud Computing

Abstract

Cloud computing provides flexible and scalable infrastructure for storing data, hosting applications, and delivering digital services. However, the increasing use of cloud platforms has also introduced security challenges, including unauthorized access, malicious network activity, data breaches, and distributed attacks. Traditional security mechanisms often depend on predefined rules and known attack patterns, which may not identify unfamiliar or rapidly changing threats. Artificial intelligence offers new opportunities to strengthen cloud security by analyzing network traffic, user activities, and system behavior to identify suspicious patterns. This paper presents an AI-based approach for intrusion detection and security threat analysis in cloud computing environments. The proposed approach collects relevant security information, processes network and system data, and applies machine learning techniques to classify normal and potentially malicious activities. The methodology considers detection accuracy, precision, recall, and false alarm rates to evaluate security performance. It also examines how intelligent threat analysis can support faster security responses and improve the protection of cloud resources. The proposed approach provides a framework for developing adaptive intrusion detection systems that can respond to changing security conditions. Its effectiveness depends on the quality of training data, model performance, and the ability to distinguish legitimate activities from malicious behavior.