MASc Seminar: Forensic Analysis in Access Control: a Case-Study of a Cloud ApplicationExport this event to calendar

Tuesday, November 19, 2019 — 1:30 PM EST

Candidate: Xiaowei Huang

Title: Forensic Analysis in Access Control: a Case-Study of a Cloud Application

 

Date: November 19, 2019

Time: 1:30

Place: EIT 3145

Supervisor(s): Tripunitara, Mahesh

 

Abstract:

We discuss a case-study we have conducted on forensic analysis in access control. The case-study is an application in the Amazon Web Services (AWS) cloud provider. Forensic analysis is the investigation and analysis of evidence of possible wrongdoing. Access control is used to regulate accesses to computing resources. Both forensic analysis and access control are recognized as important aspects of the security of a system. We first argue that posing the forensic analysis problem in the context of access control is meaningful and useful towards the security of a system. We then summarize results on the computational hardness of the forensic analysis problem for two access control schemes from the research literature. We point out that these results suggest that meaningful logging information can render forensic analysis tractable, even efficient. We then instantiate the forensic analysis in access control problem in the context of a cloud application. A cloud application is a software service that can be accessed over the Internet and uses computing resources provided by a cloud provider. A cloud provider provides computing tools and services that can be administered over the Internet. The cloud provider we have adopted is AWS, and the application is ``Hello Retail'', an image-sourcing application for online retailers. In addressing forensic analysis in this context, our particular focus is the manner in which logging information can be leveraged. We ask two kinds of questions: (i) is particular logging information from AWS necessary to answer forensics analysis questions of interest, and, (ii) is particular logging information sufficient? We observe that from the standpoint of (i), default AWS logs have considerable redundancy. We propose an algorithm to prune logs for efficient forensic analysis. From the standpoint of (ii), we observe that it is not possible to definitively answer "yes" or "no" to forensic analysis questions of interest given only the information AWS permits us to log. We identify additional logging information that, if available, would be sufficient. Together, (i) and (ii) provide us with "goal-directed logging". We conclude by reiterating the benefits of forensic analysis in access control, and with suggestions for goal-directed logging in cloud systems.

 

Location 
EIT
Room 3145
200 University Avenue West

Kitchener, ON N2L 3G1
Canada

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