53,310 research outputs found
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs
Binary code analysis allows analyzing binary code without having access to
the corresponding source code. A binary, after disassembly, is expressed in an
assembly language. This inspires us to approach binary analysis by leveraging
ideas and techniques from Natural Language Processing (NLP), a rich area
focused on processing text of various natural languages. We notice that binary
code analysis and NLP share a lot of analogical topics, such as semantics
extraction, summarization, and classification. This work utilizes these ideas
to address two important code similarity comparison problems. (I) Given a pair
of basic blocks for different instruction set architectures (ISAs), determining
whether their semantics is similar or not; and (II) given a piece of code of
interest, determining if it is contained in another piece of assembly code for
a different ISA. The solutions to these two problems have many applications,
such as cross-architecture vulnerability discovery and code plagiarism
detection. We implement a prototype system INNEREYE and perform a comprehensive
evaluation. A comparison between our approach and existing approaches to
Problem I shows that our system outperforms them in terms of accuracy,
efficiency and scalability. And the case studies utilizing the system
demonstrate that our solution to Problem II is effective. Moreover, this
research showcases how to apply ideas and techniques from NLP to large-scale
binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium
201
Combining Static and Dynamic Analysis for Vulnerability Detection
In this paper, we present a hybrid approach for buffer overflow detection in
C code. The approach makes use of static and dynamic analysis of the
application under investigation. The static part consists in calculating taint
dependency sequences (TDS) between user controlled inputs and vulnerable
statements. This process is akin to program slice of interest to calculate
tainted data- and control-flow path which exhibits the dependence between
tainted program inputs and vulnerable statements in the code. The dynamic part
consists of executing the program along TDSs to trigger the vulnerability by
generating suitable inputs. We use genetic algorithm to generate inputs. We
propose a fitness function that approximates the program behavior (control
flow) based on the frequencies of the statements along TDSs. This runtime
aspect makes the approach faster and accurate. We provide experimental results
on the Verisec benchmark to validate our approach.Comment: There are 15 pages with 1 figur
Fault Localization Models in Debugging
Debugging is considered as a rigorous but important feature of software
engineering process. Since more than a decade, the software engineering
research community is exploring different techniques for removal of faults from
programs but it is quite difficult to overcome all the faults of software
programs. Thus, it is still remains as a real challenge for software debugging
and maintenance community. In this paper, we briefly introduced software
anomalies and faults classification and then explained different fault
localization models using theory of diagnosis. Furthermore, we compared and
contrasted between value based and dependencies based models in accordance with
different real misbehaviours and presented some insight information for the
debugging process. Moreover, we discussed the results of both models and
manifested the shortcomings as well as advantages of these models in terms of
debugging and maintenance.Comment: 58-6
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