ABSTRACT
Contract cheating has become a profound issue in academics with the onset of the COVID-19 pandemic as digitised evaluation has become common practice. This evaluation method opens up for examining students remotely, either by online home exams or longer written assessments done away from the classroom. Contract cheating refers to a problem where the students hire a third party to complete their assignment and submit it for grading as their own. Manually dealing with contract cheating is a cumbersome task and tools for plagiarism detection are not able to detect contract cheaters as students do not use the work of other authors without consent. In this paper, a machine learning based system is designed to specifically detect the cases of contract cheating in academics. The system uses keystroke biometric behaviour where typing style is analysed to discriminate cheaters from genuine students. The experiments are conducted on two datasets where one is existing and another is designed by performing data collection in a university for recording the keystroke features. Two categories of keystroke dynamics, namely duration and latency-based features are studied for designing the various machine learning-based systems for investigating the efficient one. Furthermore, the performance of the systems are evaluated under the setting of zero false accusations in order to avoid genuine students being charged as imposters. © 2022 IEEE.
ABSTRACT
Phishing is a cybercrime that is increasing exponentially day by day. In phishing, a phisher employs social engineering and technology to misdirect victims towards revealing their personal information, which can then be exploited. Despite ongoing research to find effective anti-phishing solutions, phishing remains a serious security problem for Internet users. In this paper, an investigation of using CAPTCHA keystroke dynamics to enhance the prevention of phishing attacks was presented. A controlled laboratory experiment was conducted, with the results indicating the proposed approach as highly effective in protecting online services from phishing attacks. The results showed a 0% false-positive rate and 17.8% false-negative rate. Overall, the proposed solution provided a practical and effective way of preventing phishing attacks.