Anomaly Detection in Disaster Recovery: A Review, Current Trends and New Perspectives

Abstract

Anomaly detection is the process of identifying and monitoring anomalies in data for potential abnormal behavior. These can be identifiable patterns, indications of a problem, or even evidence of an attack. The motive of this survey is to offer a based and comprehensive assessment of anomaly detection studies. We labeled current techniques into diverse classes based on their underlying perspective. One way to use anomaly detection to survive a natural IT disaster is by knowing how to interpret and interpret data. This can include understanding the meaning behind anomalies, determining what caused them, and verifying that the information is real and not fake. Additionally, using regular software updates and patches can help protect your systems from potential attacks. Anomaly detection can help you identify incorrect data when collecting information. By checking the data against schedule and expected values, you can determine if any patterns exist that may suggest an error in the data collection process. We will describe a general method for detecting anomalies within a category, and then evaluate the usefulness of existing techniques in that category for a variety of applications. One way to use anomaly detection to survive a natural IT disaster is by knowing how to interpret and interpret data. This can include understanding the meaning behind anomalies, determining what caused them, and verifying that the information is real and not fake. Additionally, using regular software updates and patches can help protect your systems from potential attacks. When it comes to data collection, accuracy is key. To ensure accurate data, you need to be able to detect anomalies. Anomaly detection is the process of identifying unusual or unauthorized changes to data. Anomaly detection can help you identify incorrect data when collecting information. By checking the data against schedule and expected values, you can determine if any patterns exist that may suggest an error in the data collection process. This paper discusses the demanding situations of anomaly detection of anomalies in data collection events can help organizations survive natural IT disasters. By using anomaly detection techniques, you can detect incorrect data, out-of-date data, and breaches. In addition, by following tips for detecting anomalies in natural disasters, you can help prevent or mitigate damage. A significant portion of this paper is focused on categorizing and evaluating existing anomaly detection techniques in disaster recovery, with the goal of understanding their individual characteristics and applications.