Type
Text
Type
Thesis
Advisor
Sekar, R
Date
2012-12-01
Keywords
Computer science | Abstract Interpretation, Static Binary Analysis, Type Inference
Department
Department of Computer Science
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71535
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
In recent years, many research efforts have been dedicated to detect vulnerabilities in software. Most of these techniques are based on source code analysis. However, source code-based analysis methods are ineffective when the program source code is not available. In such a case, binary analysis is the only option. Yet, all binary analysis methods have to address serious challenges such as indirect memory access, missing functions and data abstraction. Historically, these problems have been addressed using rather ad hoc techniques. However, recent research has begun to reverse this trend. In this thesis, we cover Value-Set Analysis (VSA) and Abstract Stack Analysis (ASA) that use abstract interpretation to address aforementioned challenges in a principled way. We then move on to binary analysis methods that try to recover the missing type information in binaries. We describe TIE, Howard and REWARD as three binary type analysis methods and compare their effectiveness. | 34 pages
Recommended Citation
Saberi, Alireza, "Using Type Inference and Abstract Interpretation for Static Binary Analysis" (2012). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 740.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/740