报告主题：Novel Biometric-Based Cybersecurity Schemes for Risk-Based Authentication in Web Systems
报告时间：2024年10月11号（周五）14:00
报告地点：开发区校区综合楼204
报告摘要：
Biometrics represents one of the most robust and reliable forms of human identification in physical and cyber security. The last decade has witnessed tremendous advances in sensor technologies and data processing techniques and algorithms. This has led to the strengthening of traditional biometrics technologies (e.g., fingerprint, face, iris, retina, keystroke dynamics, mouse gestures/dynamics and voice) and the emergence of several new technologies, which are showing great promises. The confluence of the consumer markets and national security needs have led to a growing demand for biometrics products and services. For instance, the integration of biometric sensors in smartphones and the use of these technologies for online banking have boosted the adoption of biometric technologies for the masses.
Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed, and as such, put in question the robustness and reliability of the proposed systems.
Risk-based authentication can be applied from two different perspectives: proactively and reactively. When applied proactively, risk-based authentication can be integrated with the login process and used to block from the beginning access to users flagged as risky. In contrast, reactive risk-based authentication can be used to identify and revert ongoing or completed transactions considered as risky.
In this talk, we present our biometrics-based security schemes that are based on keystroke dynamics, which are considered breakthrough techniques. We them introduce our new online biometric risk-based authentication system that provides more robust user identity information by combining mouse dynamics and keystroke dynamics biometrics in a multimodal framework. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 8.21%, which is promising considering that we are dealing with free text and free mouse movements, and the fact that many web sessions tend to be very short.
报告人简介：
Professor Mohammad S. Obaidat received his Ph.D. degree in Computer Engineering with a minor in Computer Science from The Ohio State University, Columbus, USA. He is now a Distinguished Professor at KASIT, University of Jordan. He has published To Date (2023) over One Thousand (1,200) refereed technical articles-About half of them are journal articles, over 100 books, and over 70 Book Chapters. He is Editor-in-Chief of 3 scholarly journals and an editor of many other international journals. He is the founding Editor-in Chief of Wiley Security and Privacy Journal. Moreover, he is founder or co-founder of 5 IEEE International Conferences. Obaidat is a Life Fellow of IEEE, a Fellow of AAIA, Fellow of FTRA, Fellow of AIIA, and a Fellow of SCS.
