Data Analytics Methods for Anomaly Detection: Evolution and Recommendations

Big Data-based applications have been increased especially those which utilize anomaly detection techniques. This paper puts a new insight into the anomaly detection techniques, suitable for Big Data applications. This study is supported by novel classifications and practical based implementation. Three classifications are proposed for anomaly detection techniques that are aligned with Big Data characteristics and powered by several applications of the machine learning techniques, such as Support Vector Machine and neural network. This has helped to evaluate and recommend for the best practices in anomaly detection and hence a new implementation has been provided.