A Systematic analysis on Security and Anomaly Detection using Machine Learning in Cloud Computing

The term “cloud computing” describes a way of using the Internet to access resources, software, and databases without being constrained by local hardware. Businesses adopting this technology can grow their operations more flexibly by outsourcing all or a portion of the management of their infrastructure to outside hosting companies. Cloud-based security services are increasingly being used by contemporary enterprises. The dynamic and unpredictable environment, particularly in online applications and apps for the general public, can provide a number of issues even when organizations fully resource their departments. These as-a-service business models let companies outsource a variety of time-consuming IT-related jobs. The traditional intended approaches have significant difficulties with data loss prevention, anomaly detection, and data analysis. Moreover, the current cloud computing system has shortcomings in the areas of causal effect analytics, anomaly detection, and data loss prevention. The machine learning technique greatly increases the effectiveness of anomaly detection in a cloud computing network. This research effort introduces a novel approach to deal with the system’s constraints in its current state. The suggested work introduces proactive security mechanisms to stop the loss of sensitive data. A signature system based on machine learning is utilised to detect anomalies.