Efficient fuzz testing leveraging input, code, and execution

Author(s):  
Nikolas Havrikov
Keyword(s):  
Author(s):  
Huning Dai ◽  
Christian Murphy ◽  
Gail E. Kaiser

Many software security vulnerabilities only reveal themselves under certain conditions, that is, particular configurations and inputs together with a certain runtime environment. One approach to detecting these vulnerabilities is fuzz testing. However, typical fuzz testing makes no guarantees regarding the syntactic and semantic validity of the input, or of how much of the input space will be explored. To address these problems, the authors present a new testing methodology called Configuration Fuzzing. Configuration Fuzzing is a technique whereby the configuration of the running application is mutated at certain execution points to check for vulnerabilities that only arise in certain conditions. As the application runs in the deployment environment, this testing technique continuously fuzzes the configuration and checks “security invariants’’ that, if violated, indicate vulnerability. This paper discusses the approach and introduces a prototype framework called ConFu (CONfiguration FUzzing testing framework) for implementation. Additionally, the results of case studies that demonstrate the approach’s feasibility are presented along with performance evaluations.


Author(s):  
Ceyhun Ozgur ◽  
Sanjeev Jha ◽  
Bennie B. Myer-Tyson ◽  
David Booth

R has grown tremendously over the years in terms of number of users and capability with the development of hundreds of packages. In this chapter, the authors investigate the usage of R in finance and banking areas. They begin with a comparative analysis of R with other computing software like SAS and Python. Then they discuss the reasons for the growth of R's usage in financial sector. They end with a comparative evaluation of Python and R's strengths and weaknesses in a classroom. R is software designed to run statistical analyses and output graphics by user-input code. It can run on virtually any operating system and is open source. This makes the software highly appealing, as it is able to keep up with the demands of a growing number of varied business structures. Standard software has been SAS and Python; however, a growing number of jobs are posted looking for experience using R in the data analytics field.


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