The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites with Dr. Ahmed Abbasi
Please join the Institute for Financial Services Analytics for a presentation by Dr. Ahmed Abbasi, Associate Dean and Murray Research Professor McIntire School of Commerce, at the University of Virginia. His topic will be The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites.
User susceptibility to phishing websites is a significant security concern for organizations, both in terms of threats targeting employees and customer-facing attacks that undermine trust, satisfaction, and brand equity. At the root of the problem is the fact that users are ineffective at identifying and avoiding phishing websites. Even when using protective anti-phishing tools, many users remain vulnerable.
In this study, we propose the Phishing Funnel Model (PFM), a robust design artifact for predicting user susceptibility to phishing websites. Leveraging the observe-orient-decide-act (OODA) loop for decision-making in risky, adversarial, real-time environments, PFM incorporates relevant user, threat, and tool-related factors to predict decisions and actions pertaining to four key stages of the phishing process: visit, browse, consider legitimate, and intention to transact. In order to estimate the model, we used a cumulative link mixed model and support vector ordinal regression with a custom kernel for representing users’ decisions across funnel stages.
Ahmed Abbasi is Associate Dean and Murray Research Professor in the McIntire School of Commerce at the University of Virginia. He is Director of the Center for Business Analytics, co-Director of the MSBA program, and coordinator for McIntire’s executives-on-grounds program. Ahmed received his Ph.D. in Information Systems from the University of Arizona, where he also worked as a project-lead in the Artificial Intelligence Lab. He attained an M.B.A. and B.S. from Virginia Tech. Ahmed has fifteen years of experience pertaining to predictive analytics, with applications in online fraud and security, text mining, and social media. Ahmed’s research has been funded through over a dozen grants from the National Science Foundation. He has also received the IBM Faculty Award, IEEE ITS Society Research Excellence Award, AWS Research Grant, and Microsoft Research Azure Award for his work on Big Data. Moreover, his center has received over 20 grants from industry. He has published over 80 peer-reviewed articles. One of his articles was considered a top publication by the AIS. He also won best paper awards at MISQ and WITS. Ahmed’s work has been featured in various media outlets, including the Wall Street Journal, the Associated Press, WIRED, and CBS. Ahmed currently serves as SE at ISR and AE for ACM TMIS and IEEE Intelligent Systems. He is a senior member of the IEEE and has been on organizing/program committees for various conferences related to text analytics and data mining. He is a co-founder or advisory board member for multiple predictive analytics-related companies.
Friday, February 15, 2019 at 10:30am to 12:00pm
One South Main, 120 1 South Main Street, Newark, DE 19716