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

LLM-xApp: A Large Language Model Empowered Radio Resource Management...

Xingqi Wu (University of Michigan-Dearborn), Junaid Farooq (University of Michigan-Dearborn), Yuhui Wang (University of Michigan-Dearborn), Juntao Chen (Fordham University)

Read More

ScopeVerif: Analyzing the Security of Android’s Scoped Storage via...

Zeyu Lei (Purdue University), Güliz Seray Tuncay (Google), Beatrice Carissa Williem (Purdue University), Z. Berkay Celik (Purdue University), Antonio Bianchi (Purdue University)

Read More

ProvGuard: Detecting SDN Control Policy Manipulation via Contextual Semantics...

Ziwen Liu (Beihang University), Jian Mao (Beihang University; Tianmushan Laboratory; Hangzhou Innovation Institute, Beihang University), Jun Zeng (National University of Singapore), Jiawei Li (Beihang University; National University of Singapore), Qixiao Lin (Beihang University), Jiahao Liu (National University of Singapore), Jianwei Zhuge (Tsinghua University; Zhongguancun Laboratory), Zhenkai Liang (National University of Singapore)

Read More