Tongxin Wei (Nankai University), Ding Wang (Nankai University), Yutong Li (Nankai University), Yuehuan Wang (Nankai University)

Risk-based authentication (RBA) is gaining popularity and RBA notifications promptly alert users to protect their accounts from unauthorized access. Recent research indicates that users can identify legitimate login notifications triggered by themselves. However, little attention has been paid to whether RBA notifications triggered by non-account holders can effectively raise users' awareness of crises and prevent potential attacks. In this paper, we invite 258 online participants and 15 offline participants to explore users' perceptions, reactions, and expectations for three types of RBA notifications (i.e., RBA notifications triggered by correct passwords, incorrect passwords, and password resets).

The results show that over 90% of participants consider RBA notifications important. Users do not show significant differences in their feelings and behaviors towards the three types of RBA notifications, but they have distinct expectations for each type. Most participants feel suspicious, nervous, and anxious upon receiving the three types of RBA notifications not triggered by themselves. Consequently, users immediately review the full content of the notification. 46% of users suspect that RBA notifications might be phishing attempts, while categorizing them as potential phishing attacks or spam may lead to ineffective account protection. Despite these suspicions, 65% of users still log into their accounts to check for suspicious activities and take no further action if no abnormalities are found. Additionally, the current format of RBA notifications fails to gain users' trust and meet their expectations. Our findings indicate that RBA notifications need to provide more detailed information about suspicious access, offer additional security measures, and clearly explain the risks involved. Finally, we offer five design recommendations for RBA notifications to better mitigate potential risks and enhance account security.

View More Papers

VoiceRadar: Voice Deepfake Detection using Micro-Frequency and Compositional Analysis

Kavita Kumari (Technical University of Darmstadt), Maryam Abbasihafshejani (University of Texas at San Antonio), Alessandro Pegoraro (Technical University of Darmstadt), Phillip Rieger (Technical University of Darmstadt), Kamyar Arshi (Technical University of Darmstadt), Murtuza Jadliwala (University of Texas at San Antonio), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More

Blackbox Fuzzing of Distributed Systems with Multi-Dimensional Inputs and...

Yonghao Zou (Beihang University and Peking University), Jia-Ju Bai (Beihang University), Zu-Ming Jiang (ETH Zurich), Ming Zhao (Arizona State University), Diyu Zhou (Peking University)

Read More

Evaluating Machine Learning-Based IoT Device Identification Models for Security...

Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

Read More

Off-Path TCP Hijacking in Wi-Fi Networks: A Packet-Size Side...

Ziqiang Wang (Southeast University), Xuewei Feng (Tsinghua University), Qi Li (Tsinghua University), Kun Sun (George Mason University), Yuxiang Yang (Tsinghua University), Mengyuan Li (University of Toronto), Ganqiu Du (China Software Testing Center), Ke Xu (Tsinghua University), Jianping Wu (Tsinghua University)

Read More