Challenges In Cloud Anomaly Detection Using Machine Learning Approaches
S Prathibha, S. Vinay · 2022
Cloud computing is one of the fastest-growing technologies in the present technological world. Massive amounts of transactions, data and the hidden infrastructure of cloud computing systems pose many challenges to the research community. Cloud network security has become an important task in which anomaly detection plays an important role. Machine learning anomaly detection is a special research topic today because cloud services are exponentially growing. in various industries. As the amount of data in the cloud grows, it becomes very difficult or impossible to process it in a timely and error-free manner using only traditional mathematical approaches. Additionally, a vast amount of unstructured data of all types and formats are collected and stored in the cloud, including images, videos, and audio. Anomaly detection is a process to identify suspicious data items in both normal and unexpected events. With new and unknown attacks occurring every day, one must need an approach that can identify unknown behavior in cloud data. This paper provides an overview of setting up an anomaly detection system and issues to consider when collecting data, deploying models, and implementing suitable machine learning techniques. Therefore, this paper focuses on several future research directions that can improve the detection of cloud anomalies.