Visible to the public Biblio

Filters: Keyword is systematic analysis  [Clear All Filters]
2016-04-07
Goncalo Martins, Sajal Bhatia, Xenofon Kousoukos, Keith Stouffer, CheeYee Tang, Richard Candell.  2015.  Towards a Systematic Threat Modeling Approach for Cyber-physical Systems. 2nd National Symposium on Resilient Critical Infrastructure (ISRCS 2015).

Cyber-Physical Systems (CPS) are systems with seamless integration of physical, computational and networking components. These systems can potentially have an impact on the physical components, hence it is critical to safeguard them against a wide range of attacks. In this paper, it is argued that an effective approach to achieve this goal is to systematically identify the potential threats at the design phase of building such systems, commonly achieved via threat modeling. In this context, a tool to perform systematic analysis of threat modeling for CPS is proposed. A real-world wireless railway temperature monitoring system is used as a case study to validate the proposed approach. The threats identified in the system are subsequently mitigated using National Institute of Standards and Technology (NIST) standards.

2015-05-05
Guowei Dong, Yan Zhang, Xin Wang, Peng Wang, Liangkun Liu.  2014.  Detecting cross site scripting vulnerabilities introduced by HTML5. Computer Science and Software Engineering (JCSSE), 2014 11th International Joint Conference on. :319-323.

Recent years, HTML5 is widely adopted in popular browsers. Unfortunately, as a new Web standard, HTML5 may expand the Cross Site Scripting (XSS) attack surface as well as improve the interactivity of the page. In this paper, we identified 14 XSS attack vectors related to HTML5 by a systematic analysis about new tags and attributes. Based on these vectors, a XSS test vector repository is constructed and a dynamic XSS vulnerability detection tool focusing on Webmail systems is implemented. By applying the tool to some popular Webmail systems, seven exploitable XSS vulnerabilities are found. The evaluation result shows that our tool can efficiently detect XSS vulnerabilities introduced by HTML5.