Usability and Security Testing of Online Links: A Framework for Click-Through Rate Prediction Using Deep Learning

• Published in: Electronics (Basel) Vol. 11; no. 3; p. 400
• Main Authors: Damaševičius, Robertas, Zailskaitė-Jakštė, Ligita
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Artificial intelligence; Cybercrime; Cybersecurity; Deep learning; Distance learning; Fraud; Internet; Machine learning; Neural networks; On-line systems; Online advertising; Recommender systems; Security systems; Social networks; Subject specialists; Usability; User behavior; Websites
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11030400

The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure decision support. Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product, which is key for understanding human online behaviour. However, online systems are prone to click on fraud attacks. We propose a Human-Centric Cyber Security (HCCS) model that additionally includes AI techniques targeted at the key elements of user, usage, and usability. As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait. The results of experiments on a real-world benchmark Avazu dataset show that the proposed approach outpaces (AUC is 0.8062) other CTR forecasting approaches, demonstrating the viability of the proposed framework.