Article 1: Cybercrime Challenges in Iraqi Academia

In the education system, there are impressionable youth who are vulnerable to cyber
attacks. This particular age group who ranges from children to young adults are some of the most active people online, which renders them a huge target for malicious people. Because of this, there was a study done to identify the factors that could contribute to increasing internet awareness within an environment of education. This plays into the social science of cybersecurity, as this awareness of the web will allow them to possibly thrive in a rapidly changing cyber environment. They used descriptive statistics and regression analysis to organize their findings. To determine the level of awareness among the youth in the study, there were 5 independent variables such as information security, creative behavior, cyber education, cyber training, and internet application, with the dependent variable being digital awareness. The study included 140 children who were to answer a questionnaire of 25 items which all had to do with one independent variable (4 questions each) or dependent variable (5 questions). By using parametric statistical tests, hypothesis testing and regression analysis, the data concluded that each variable positively correlated with one another, implying the need for further research, an updated curriculum for cyber research, and a higher emphasis on cyber security lessons for the youth.

Article 2: Classifying social media bots as malicious or benign
Among the many areas that the youth go to when they’re on the internet, none are quite as prominent as social media. Functioning as a sort of online sanctuary for entertainment, news, and interactivity among others, social media reigns as one of the most popular places you can go online. One of these places is Twitter, where despite being controversial in many cases, remains one of the biggest platforms in the market. Unfortunately, with a big crowd of unsuspecting people, comes a crowd of malicious people attempting to benefit. Twitter, ever since its inception in 2006 has had to increase its defenses constantly due to hackers, and incorporated reporting capabilities for people such as scammers. Unfortunately, some scammers incorporate the use of bots in order to lure people into a scam. While there are many malicious bots, there are bots that are benign, which is exactly what this study is focused on. This study was to investigate which OSN features are useful for determining malicious and benign bots. The datasets within Twitter encompassing benign and malicious bots helped greatly to discover questionable actions, such as “like fraud” and “retweet” spam caused by malicious bots. After they identified notable features, four semi-supervised AD algorithms, such as GMM, S3VM, LP, and LS, were implemented in order to classify malicious and benign bots. S3VM produced the most prominent results with a recall of 89% and an F1 of 76%. This assisted them in concluding that the methods for classifying humans and malicious bots were not the same for benign and malicious bots. They believe that their findings will soon reduce cyber-related threats caused by malicious bots and improve the experience of users on social media networks, even beyond Twitter.

Links to articles

Article 1:
● https://cybercrimejournal.com/menuscript/index.php/cybercrimejournal/article/view/87/24

Article 2:
● https://academic.oup.com/cybersecurity/article/9/1/tyac015/6972135?searchresult=1#390578684