Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

No abstract

Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively.