Abstract
The popularity and usage of Cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. One of the results of Cloud development is the emergence of various security problems for both industry and consumer. One of the ways to secure Cloud is by using Machine Learning (ML). ML techniques have been used in various ways to prevent or detect attacks and security gaps on the Cloud. In this paper, we provide a Systematic Literature Review (SLR) of ML and Cloud security methodologies and techniques. We analyzed 63 relevant studies and the results of the SLR are categorized into three main research areas: (i) the different types of Cloud security threats, (ii) ML techniques used, and (iii) the performance outcomes. We have defined 11 Cloud security areas. Moreover, distributed denial-of-service (DDoS) and data privacy are the most common Cloud security areas, with a 16% level of use and 14%respectively. On the other hand, we found 30 ML techniques used, some used hybrid and others as standalone. The most popular ML used is SVM in both hybrid and standalone models. Furthermore, 60% of the papers compared their models with other models to prove the efficiency of their proposed model. Moreover, 13 different evaluation metrics were enumerated. The most applied metric is true positive rate and least used is training time. Lastly, from 20 datasets found, KDD and KDD CUP’99 are the most used among relevant studies.
Highlights
Cloud computing is a technological advance that offers the facilities, platform and software of information technology as Internet services [1]
RQ1: CLOUD SECURITY AREAS From the 63 papers we have collected, we deduce that 11 Cloud security issues are addressed and researched
We identified Machine Learning (ML) algorithms that had been applied by researchers in Cloud security areas
Summary
Cloud computing is a technological advance that offers the facilities, platform and software of information technology as Internet services [1]. Cloud computing is seen as a significant and positive IT infrastructure shift, much security work is still needed to minimize its deficiencies. Since a significant amount of personal and corporate information is placed in the Cloud data centers, those Cloud security issues and vulnerabilities need to be identified and prevented. Cloud infrastructure runs through standard Internet protocols and uses virtualization techniques, it may be vulnerable to attacks. Those attacks may come from traditional sources such as Address Resolution Protocol, IP spoofing, Denial of Service (DoS) etc., [8], [9]. Traditional techniques used for detection and prevention are not efficient enough to handle those attacks while dealing with a large data flow
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