Sirvan Almasi (Imperial College London), William J. Knottenbelt (Imperial College London)

Password composition policies (PCPs) are critical security rules that govern how users create passwords for online authentication. Despite passwords remaining the primary authentication method online, there is significant disagreement among experts, regulatory bodies, and researchers about what constitutes effective password policies. This lack of consensus has led to high variance in PCP implementations across websites, leaving both developers and users uncertain. Current approaches lack a theoretical foundation for evaluating and comparing different password composition policies. We show that a structure-based policy, such as the three-random words recommended by UK’s National Cyber Security Centre (NCSC), can improve password security. We demonstrate this using an empirical evaluation of labelled password datasets and a new theoretical framework. Using these methods we demonstrate the feasibility and security of multi-word password policy and extend the NCSC’s recommendation to five words to account for nonuniform word selection. These findings provide an evidence-based framework for password policy development and suggest that current web authentication systems should adjust their minimum word requirements upward while maintaining usability.

View More Papers

WAVEN: WebAssembly Memory Virtualization for Enclaves

Weili Wang (Southern University of Science and Technology), Honghan Ji (ByteDance Inc.), Peixuan He (ByteDance Inc.), Yao Zhang (ByteDance Inc.), Ye Wu (ByteDance Inc.), Yinqian Zhang (Southern University of Science and Technology)

Read More

Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

Read More

CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models

Rui Zeng (Zhejiang University), Xi Chen (Zhejiang University), Yuwen Pu (Zhejiang University), Xuhong Zhang (Zhejiang University), Tianyu Du (Zhejiang University), Shouling Ji (Zhejiang University)

Read More