Biblio

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2020-05-08
Saccente, Nicholas, Dehlinger, Josh, Deng, Lin, Chakraborty, Suranjan, Xiong, Yin.  2019.  Project Achilles: A Prototype Tool for Static Method-Level Vulnerability Detection of Java Source Code Using a Recurrent Neural Network. 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW). :114—121.

Software has become an essential component of modern life, but when software vulnerabilities threaten the security of users, new ways of analyzing for software security must be explored. Using the National Institute of Standards and Technology's Juliet Java Suite, containing thousands of examples of defective Java methods for a variety of vulnerabilities, a prototype tool was developed implementing an array of Long-Short Term Memory Recurrent Neural Networks to detect vulnerabilities within source code. The tool employs various data preparation methods to be independent of coding style and to automate the process of extracting methods, labeling data, and partitioning the dataset. The result is a prototype command-line utility that generates an n-dimensional vulnerability prediction vector. The experimental evaluation using 44,495 test cases indicates that the tool can achieve an accuracy higher than 90% for 24 out of 29 different types of CWE vulnerabilities.

2020-02-10
Visalli, Nicholas, Deng, Lin, Al-Suwaida, Amro, Brown, Zachary, Joshi, Manish, Wei, Bingyang.  2019.  Towards Automated Security Vulnerability and Software Defect Localization. 2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA). :90–93.

Security vulnerabilities and software defects are prevalent in software systems, threatening every aspect of cyberspace. The complexity of modern software makes it hard to secure systems. Security vulnerabilities and software defects become a major target of cyberattacks which can lead to significant consequences. Manual identification of vulnerabilities and defects in software systems is very time-consuming and tedious. Many tools have been designed to help analyze software systems and to discover vulnerabilities and defects. However, these tools tend to miss various types of bugs. The bugs that are not caught by these tools usually include vulnerabilities and defects that are too complicated to find or do not fall inside of an existing rule-set for identification. It was hypothesized that these undiscovered vulnerabilities and defects do not occur randomly, rather, they share certain common characteristics. A methodology was proposed to detect the probability of a bug existing in a code structure. We used a comprehensive experimental evaluation to assess the methodology and report our findings.