Role-Playing Game for Studying User Behaviors in Security: A Case Study on Email Secrecy
被引:2
作者:
Xu, Kui
论文数: 0引用数: 0
h-index: 0
机构:
Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USAVirginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
Xu, Kui
[1
]
Yao, Danfeng
论文数: 0引用数: 0
h-index: 0
机构:
Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USAVirginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
Yao, Danfeng
[1
]
Perez-Quinones, Manuel A.
论文数: 0引用数: 0
h-index: 0
机构:
Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USAVirginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
Perez-Quinones, Manuel A.
[1
]
Link, Casey
论文数: 0引用数: 0
h-index: 0
机构:
Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USAVirginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
Link, Casey
[1
]
Geller, E. Scott
论文数: 0引用数: 0
h-index: 0
机构:
Virginia Tech, Ctr Appl Behav Syst, Dept Psychol, Blacksburg, VA USAVirginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
Geller, E. Scott
[2
]
机构:
[1] Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
[2] Virginia Tech, Ctr Appl Behav Syst, Dept Psychol, Blacksburg, VA USA
来源:
2014 INTERNATIONAL CONFERENCE ON COLLABORATIVE COMPUTING: NETWORKING, APPLICATIONS AND WORKSHARING (COLLABORATECOM)
|
2014年
关键词:
KNOWLEDGE;
ENTROPY;
D O I:
10.4108/icst.collaboratecom.2014.257242
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
Understanding the capabilities of adversaries (e.g., how much the adversary knows about a target) is important for building strong security defenses. Computing an adversary's knowledge about a target requires new modeling techniques and experimental methods. Our work describes a quantitative analysis technique for modeling an adversary's knowledge about private information at workplace. Our technical enabler is a new emulation environment for conducting user experiments on attack behaviors. We develop a role-playing cyber game for our evaluation, where the participants take on the adversary role to launch ID theft attacks by answering challenge questions about a target. We measure an adversary's knowledge based on how well he or she answers the authentication questions about a target. We present our empirical modeling results based on the data collected from a total of 36 users.