Intrusion Detection in Cloud Architecture Using Machine Learning

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ.