abstract: Web applications are an incredibly important aspect of our modern lives. Organizations
and developers use automated vulnerability analysis tools, also known as
scanners, to automatically find vulnerabilities in their web applications during development.
Scanners have traditionally fallen into two types of approaches: black-box
and white-box. In the black-box approaches, the scanner does not have access to the
source code of the web application whereas a white-box approach has access to the
source code. Today’s state-of-the-art black-box vulnerability scanners employ various
methods to fuzz and detect vulnerabilities in a web application. However, these
scanners attempt to fuzz the web application with a number of known payloads and
to try to trigger a vulnerability. This technique is simple but does not understand
the web application that it is testing. This thesis, presents a new approach to vulnerability
analysis. The vulnerability analysis module presented uses a novel approach
of Inductive Reverse Engineering (IRE) to understand and model the web application.
IRE first attempts to understand the behavior of the web application by giving
certain number of input/output pairs to the web application. Then, the IRE module
hypothesizes a set of programs (in a limited language specific to web applications,
called AWL) that satisfy the input/output pairs. These hypotheses takes the form of
a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt
to detect vulnerabilities in this DAG. Further, it generates the payload based on the
DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability
(based on our understanding of the program). It then tests this potential
vulnerability using the generated payload on the actual web application, and creates
a verification procedure to see if the potential vulnerability is actually vulnerable,
based on the web application’s response. / Dissertation/Thesis / Masters Thesis Computer Science 2017
| Identifer | oai:union.ndltd.org:asu.edu/item:44256 |
| Date | January 2017 |
| Contributors | Khairnar, Tejas (Author), Doupé, Adam (Advisor), Ahn, Gail-Joon (Committee member), Zhao, Ziming (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 47 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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