Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques

Nowadays, e-commerce websites have become part of people’s daily lives; therefore, it has become necessary to seek help in assessing and improving the usability of the services of e-commerce websites. Essentially, usability studies offer significant information about users’ assessment and perceptions of satisfaction, effectiveness, and efficiency of online services. This research investigated the usability of two e-commerce web-sites in Saudi Arabia and compared the effectiveness of different behavioral measurement techniques, such as heuristic evaluation, usability testing, and eye-tracking. In particular, this research selected the Extra and Jarir e-commerce websites in Saudi Arabia based on a combined approach of criteria and ranking. This research followed an experimental approach in which both qualitative and quantitative approaches were employed to collect and analyze the data. Each of the behavioral measurement techniques identified usability issues ranging from cosmetic to catastrophic issues. It is worth mentioning that the heuristic evaluation by experts provided both the majority of the issues and identified the most severe usability issues compared to the number of issues identified by both usability testing and eye-tracking combined. Usability testing provided fewer problems, most of which had already been identified by the experts. Eye-tracking provided critical information regarding the page design and element placements and revealed certain user behavior patterns that indicated certain usability problems. Overall, the research findings appeared useful to user experience (UX) and user interface (UI) designers to consider the provided recommendations to enhance the usability of e-commerce websites

Cybersecurity Risk Analysis in the IoT: A Systematic Review

The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening IoT cybersecurity, as well as the proposed frameworks and solutions. Furthermore, it explores emerging trends and identifies existing gaps in this domain. The study’s novelty lies in its extensive exploration of machine learning techniques for detecting and countering IoT threats. It also contributes by highlighting research gaps in economic impact assessment and industrial IoT security. The systematic review analyzes 40 articles, providing valuable insights and guiding future research directions. Results show that privacy issues and cybercrimes are the primary concerns in IoT security, and artificial intelligence holds promise for future cybersecurity. However, some attacks remain inadequately addressed by existing solutions, such as confidentiality, security authentication, and data server connection attacks, necessitating further research and real-life testing of proposed remedies