Fidelity of Statistical Reporting in 10 Years of Cyber Security User Studies
Thomas Groß
备注：
40 pages. The short version of this paper is appearing in the Proceedings of the 9\textsuperscript{th} Workshop on Socio-Technical Aspects in Security (STAST 2019), pp. 1--24, LNCS, Springer Verlag (2020). Preregistered at the Open Science Framework: osf.io/549qn/
Studies in socio-technical aspects of security often rely on user studies and statistical inferences on investigated relations to make their case. They, thereby, enable practitioners and scientists alike to judge on the validity and reliability of the research undertaken. To ascertain this capacity, we investigated the reporting fidelity of security user studies. Based on a systematic literature review of $114$ user studies in cyber security from selected venues in the 10 years 2006--2016, we evaluated fidelity of the reporting of $1775$ statistical inferences using the \textsf{R} package \textsf{statcheck}. We conducted a systematic classification of incomplete reporting, reporting inconsistencies and decision errors, leading to multinomial logistic regression (MLR) on the impact of publication venue/year as well as a comparison to a compatible field of psychology. We found that half the cyber security user studies considered reported incomplete results, in stark difference to comparable results in a field of psychology. Our MLR on analysis outcomes yielded a slight increase of likelihood of incomplete tests over time, while SOUPS yielded a few percent greater likelihood to report statistics correctly than other venues. In this study, we offer the first fully quantitative analysis of the state-of-play of socio-technical studies in security. While we highlight the impact and prevalence of incomplete reporting, we also offer fine-grained diagnostics and recommendations on how to respond to the situation.
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning
Delft University of Technology（代尔夫特理工大学）；DNV Netherlands B.V.（DNV荷兰有限公司）；Elia Group Innovation（Elia集团创新中心）；University of Michigan–Dearborn（密歇根大学迪尔伯恩分校）
Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
Cloud and Embedded Platform, Traton AB（云和嵌入式平台，Traton AB）；Department of Electrical And Information Technology, LTH, Lund University（电气与信息科技系，LTH，吕勒欧大学）
本文针对车载网络攻击数据稀缺问题，提出一种上下文感知攻击数据生成器，可生成多种 CAN 攻击日志，并通过 IDS 案例验证了生成数据的有效性与实用性。
