Proficient Machine Learning Techniques for a Secured Cloud Environment
Majjaru Chandrababu1, Senthil Kumar K2
1Majjaru Chandrababu, School of Information and Technology, Vellore Institute of Technology, Vellore, India.
2Dr. Senthil Kumar K, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on 19 July 2022 | Revised Manuscript received on 22 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 74-81 | Volume-11 Issue-6, August 2022 | Retrieval Number: 100.1/ijeat.F37300811622 | DOI: 10.35940/ijeat.F3730.0811622
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Many different checks, rules, processes, and technologies work together to keep cloud-based applications and infrastructure safe and secure against cyberattacks. Data security, customer privacy, regulatory enforcement, and device and user authentication regulations are all protected by these safety measures. Insecure Access Points, DDoS Attacks, Data Breach and Data Loss are the most pressing issues in cloud security. In the cloud computing context, researchers looked at several methods for detecting intrusions. Cloud security best practises such as host & middleware security, infrastructure and virtualization security, and application system & data security make up the bulk of these approaches, which are based on more traditional means of detecting abuse and anomalies. Machine Learning-based strategies for securing cloud infrastructure are the topic of this work, and ongoing research comprises research issues. There are a number of unresolved issues that will be addressed in the future.
Keywords: Cloud Computing, Anomaly Detection, Machine Learning Approaches, Supervised Learning and Unsupervised-Learning.
Scope of the Article: Machine Learning