(untitled) (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2025)
With the increasing adoption of cloud computing across industries, ensuring robust security measures has become a critical priority. Traditional security approaches, such as rule-based intrusion detection systems and signature-based methods, often fail to detect novel and sophisticated cyber threats in real-time. As a result, the integration of machine learning (ML) for anomaly detection has emerged as a powerful solution for enhancing cloud security. This paper explores the implementation of ML-based anomaly detection techniques to identify and mitigate security threats in cloud environments. Specifically, it examines various ML approaches, including supervised, unsupervised, and reinforcement learning, and their effectiveness in detecting deviations from normal system behavior. By analyzing patterns in network traffic, user activity, and system logs, ML models can identify potential threats such as insider attacks, unauthorized access, malware infiltration, and distributed denial-of-service (DDoS) attacks. Furthermore, the paper discusses the challenges associated with ML-driven security solutions, including the need for high-quality training data, computational overhead, model interpretability, and potential adversarial attacks on learning algorithms. Additionally, privacy concerns related to data collection and processing in cloud environments are highlighted. Despite these challenges, ML-based anomaly detection offers significant advantages over conventional security mechanisms by enabling adaptive, scalable, and proactive threat detection. Through an in-depth review of recent advancements and case studies, this research underscores the transformative potential of ML in strengthening cloud security. By leveraging artificial intelligence and data-driven anomaly detection techniques, cloud service providers can improve their security posture, reduce false positives in threat detection, and enhance real-time incident response. Ultimately, this study advocates for the integration of ML-based anomaly detection as a fundamental component of modern cloud security frameworks to ensure the resilience and integrity of cloud-based systems against evolving cyber threats.