2019
1.
Nembhard, Fitzroy D.; Carvalho, Marco M.; Eskridge, Thomas C.
Towards the Application of Recommender Systems to Secure Coding Journal Article
In: EURASIP Journal on Information Security, vol. 2019, no. 1, pp. 9, 2019, ISBN: 2510-523X.
Abstract | Links | BibTeX | Tags: ab testing, bugs, code security, intellisense, java, minhash, recommender systems, simhash, software quality, user study, vulnerability detection
@article{nembhard_recommender_journal,
title = {Towards the Application of Recommender Systems to Secure Coding},
author = {Fitzroy D. Nembhard and Marco M. Carvalho and Thomas C. Eskridge},
url = {https://doi.org/10.1186/s13635-019-0092-4},
doi = {10.1186/s13635-019-0092-4},
isbn = {2510-523X},
year = {2019},
date = {2019-06-13},
urldate = {2019-06-13},
journal = {EURASIP Journal on Information Security},
volume = {2019},
number = {1},
pages = {9},
abstract = {Secure coding is crucial for the design of secure and efficient software and computing systems. However, many programmers avoid secure coding practices for a variety of reasons. Some of these reasons are lack of knowledge of secure coding standards, negligence, and poor performance of and usability issues with existing code analysis tools. Therefore, it is essential to create tools that address these issues and concerns. This article features the proposal, development, and evaluation of a recommender system that uses text mining techniques, coupled with IntelliSense technology, to recommend fixes for potential vulnerabilities in program code. The resulting system mines a large code base of over 1.6 million Java files using the MapReduce methodology, creating a knowledge base for a recommender system that provides fixes for taint-style vulnerabilities. Formative testing and a usability study determined that surveyed participants strongly believed that a recommender system would help programmers write more secure code.},
keywords = {ab testing, bugs, code security, intellisense, java, minhash, recommender systems, simhash, software quality, user study, vulnerability detection},
pubstate = {published},
tppubtype = {article}
}
Secure coding is crucial for the design of secure and efficient software and computing systems. However, many programmers avoid secure coding practices for a variety of reasons. Some of these reasons are lack of knowledge of secure coding standards, negligence, and poor performance of and usability issues with existing code analysis tools. Therefore, it is essential to create tools that address these issues and concerns. This article features the proposal, development, and evaluation of a recommender system that uses text mining techniques, coupled with IntelliSense technology, to recommend fixes for potential vulnerabilities in program code. The resulting system mines a large code base of over 1.6 million Java files using the MapReduce methodology, creating a knowledge base for a recommender system that provides fixes for taint-style vulnerabilities. Formative testing and a usability study determined that surveyed participants strongly believed that a recommender system would help programmers write more secure code.
2017
2.
Nembhard, Fitzroy; Carvalho, Marco; Eskridge, Thomas
A Hybrid Approach to Improving Program Security Proceedings Article
In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017.
Abstract | BibTeX | Tags: code security, cybersecurity, recommender systems, topic modeling, vulnerability detection, vulnerability mitigation
@inproceedings{nembhard_hybrid_2017,
title = {A Hybrid Approach to Improving Program Security},
author = {Fitzroy Nembhard and Marco Carvalho and Thomas Eskridge},
year = {2017},
date = {2017-11-27},
urldate = {2017-11-27},
booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)},
abstract = {The security of computer programs and systems is a very critical issue. With the number of attacks launched on computer networks and software, businesses and IT professionals are taking steps to ensure that their information systems are as secure as possible. However, many programmers do not think about adding security to their programs until their projects are near completion. This is a major mistake because a system is as secure as its weakest link. If security is viewed as an afterthought, it is highly likely that the resulting system will have a large number of vulnerabilities, which could be exploited by attackers. One of the reasons programmers overlook adding security to their code is because it is viewed as a complicated or time-consuming process. This paper presents a tool that will help programmers think more about security and add security tactics to their code with ease. We created a model that learns from existing open source projects and documentation using machine learning and text mining techniques. Our tool contains a module that runs in the background to analyze code as the programmer types and offers suggestions of where security could be included. In addition, our tool fetches existing open source implementations of cryptographic algorithms and sample code from repositories to aid programmers in adding security easily to their projects.},
keywords = {code security, cybersecurity, recommender systems, topic modeling, vulnerability detection, vulnerability mitigation},
pubstate = {published},
tppubtype = {inproceedings}
}
The security of computer programs and systems is a very critical issue. With the number of attacks launched on computer networks and software, businesses and IT professionals are taking steps to ensure that their information systems are as secure as possible. However, many programmers do not think about adding security to their programs until their projects are near completion. This is a major mistake because a system is as secure as its weakest link. If security is viewed as an afterthought, it is highly likely that the resulting system will have a large number of vulnerabilities, which could be exploited by attackers. One of the reasons programmers overlook adding security to their code is because it is viewed as a complicated or time-consuming process. This paper presents a tool that will help programmers think more about security and add security tactics to their code with ease. We created a model that learns from existing open source projects and documentation using machine learning and text mining techniques. Our tool contains a module that runs in the background to analyze code as the programmer types and offers suggestions of where security could be included. In addition, our tool fetches existing open source implementations of cryptographic algorithms and sample code from repositories to aid programmers in adding security easily to their projects.