Electronic mail (Email) is one of the official and very common way of exchanging data and information over digital and electronic devices. Millions of users worldwide use email to exchange data and information between email servers. On the other hand, unwanted emails or spam became phenomenon challenging major companies and organizations due to the volume of spam which is increasing dramatically every year. Spam is annoying and may contain harmful contents. In addition, spam consume computers, servers, and network resources, causes harmful bottleneck, effect on computing memory and speed of digital devices. Moreover, the time consumed by the users to remove unwanted emails is huge. There are many methods developed to filter spam like keyword matching blacklist/whitelist and header information processing. Though, classical methods like blocking the source to prevent the spam are not effective. This study demonstrates and reviews the performance evaluation of the most popular and effective machine learning techniques and algorithms such as Support Vector Machine, ANN, J48, and Naïve Bayes for email spam classification and filtering. In con conclusion, support vector machine performs better than any individual algorithm in term of accuracy. This research contributes on the for the development of methods and techniques for better detection and prevention of spam.

COVID-19 pandemic has changed the lifestyle of all aspects of life. These circumstances have created new patterns in lifestyle that people had to deal with. As such, full and direct dependence on the use of the unsafe Internet network in running all aspects of life. As example, many organizations started officially working through the Internet, students moved to e-education, online shopping increased, and more. These conditions have created a fertile environment for cybercriminals to grow their activity and exploit the pressures that affected human psychology to increase their attack success. The purpose of this paper is to analyze the data collected from global online fraud and cybersecurity service companies to demonstrate on how cybercrimes increased during the COVID-19 epidemic. The significance and value of this research is to highlight by evident on how criminals exploit crisis, and for the need to develop strategies and to enhance user awareness for better detection and prevention of future cybercrimes.