Anomaly Detection Techniques - Challenges and Solutions: Analyzing anomaly detection techniques for identifying unusual patterns or outliers in datasets and addressing challenges

Abstract

Anomaly detection plays a crucial role in various fields such as cybersecurity, fraud detection, healthcare monitoring, and industrial automation. This paper provides a comprehensive review of anomaly detection techniques, focusing on their challenges and solutions. We discuss the types of anomalies, common evaluation metrics, and challenges faced in real-world applications. Furthermore, we analyze various anomaly detection methods, including statistical, machine learning, and deep learning approaches, highlighting their strengths and limitations. Additionally, we discuss strategies to address challenges such as imbalanced data, interpretability, and scalability. Finally, we present future research directions to improve anomaly detection techniques for emerging applications.