Intrusion Detection using Machine Learning

Aim: This paper aims to evaluate how machine learning is used by many companies to protect and detect various attacks and secure data on the cloud. Background: In this paper, a systematic review is provided of machine learning and its various techniques which are helpful in making the data on cloud secure from various threats such as denial of service attack and data privacy is one of the most common areas in cloud security which should be taken in consideration to protect the data and make cloud secure for every user. Objective: The paper elicitates the requirement of machine learning and identifying various techniques which are used to protect the data on cloud and make it secure for its users to store their data on cloud. Methods: The paper starts with discussing about machine learning and cloud security as an important service provided by cloud. This paper evaluates the potential of machine learning through a case study and discussing techniques in brief which help to make cloud secure for storing data. Also various recent attacks are also analyzed in order to prevent attacks in cloud and provide various methods to improve it. Result: Machine learning plays a key role in enhancing the cloud security capabilities, a platform that uses machine learning in the best possible manner to improve privacy by easily adapting evolving changes. Conclusion: The idea of adapting machine learning in cloud security is prominent and has proved to be. There are various techniques such as vector machines and many others which are widely used in cloud security and also different fields such as in networking. In this paper we have discussed about various other techniques which are preventing and detecting attacks in the cloud.