Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques - Cavallaro, Claudia; Cutello, Vincenzo; Pavone, Mario; Zito, Francesco (Frontiers in Big Data, 2023)
With the increase in available data from computer systems and their security threats, interest in Anomaly Detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers on big data, as manual labeling would be difficult to practice due to their huge volume. The results obtained from the data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, Anomaly Detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in literature to classify unknown anomalies in log analysis, process analysis and time series have been mainly based on Machine Learning and Deep Learning techniques.In this study we provide an overview of current state-of-the-art methodologies highlighting their advantages and disadvantages and the new challenges. We will see that as a consequence of the given dataset we may have a different method that achieves the best result. Finally, we describe how the use of Metaheuristics within Machine Learning algorithms makes it possible to have a more robust and efficient tool.