Explainable Machine Learning: A brief survey on history, research areas, approaches and challenges

As cloud services increasingly become the backbone of modern enterprises, ensuring their security has become critical to mitigating risks associated with cyber threats. Traditional security measures often fall short in addressing the dynamic and complex nature of cloud environments. Machine Learning (ML) offers a powerful solution to these challenges by enabling proactive threat detection, anomaly identification, and adaptive response mechanisms. This paper explores the integration of ML techniques in cloud service security, focusing on real-time monitoring, automated threat detection, and behavior-based anomaly detection systems. Through supervised and unsupervised learning models, we present a taxonomy of ML-driven security approaches, including intrusion detection, malware identification, and predictive analytics for vulnerability assessment. Furthermore, we discuss the scalability of ML-based security models in multi-tenant cloud infrastructures and highlight the challenges of data privacy, model interpretability, and the balance between performance and accuracy. This study underscores the role of ML as a transformative technology in enhancing cloud service security, paving the way for more resilient and intelligent cloud ecosystems. The research focuses on the investigation and analysis of AI in machine learning. Understanding the foundational ideas, techniques, and applications of AI around machine learning was the goal of this study. A review of artificial intelligence and its importance in the current technological environment opens the project. The fundamental ideas of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning, are then covered in depth. several machines learning techniques, including decision trees. The research continues to investigate how AI is applied in various fields. To emphasize the useful applications of AI in machine learning, real-world examples and case studies are provided. The research also includes crucial material on subjects like AI data preparation to aid in comprehension. In addition, future possibilities and new developments in AI and machine learning are examined.