Can traditional fault prediction models be used for vulnerability prediction?

Shin, Yonghee; Williams, Laurie

doi:10.1007/s10664-011-9190-8

Cited by 170 publications

(109 citation statements)

References 35 publications

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“…With respect to the software metrics approach, we found different results from all the other studies [8], [13], [6], i.e., better precision and significantly lower recall. This difference could be explained by the way we construct our datasets and/or by the fact that undersampling is used in these studies to balance the datasets which may have impacted the drawn result.…”

Section: B Differences With Previous Studiescontrasting

confidence: 87%

“…This is quite encouraging since it suggests that vulnerability prediction models can be useful and practical. As shown in Figures 4 and 5 the top performing prediction models achieve precision values of approximately 75% with recall of approximately 50%, which are judged by the studies of Morrison et al [10] and Shin et al [13] to be satisfactory. Interestingly we found a small influence of the imbalanced data (as shown by the differences between Figures 4 and 5) on our results.…”

Section: A Implicationsmentioning

confidence: 75%

“…For example: Neuhaus et al [7] and Shin et al [13] used Mozilla Firefox Security Announcement (MFSA) to build their vulnerability dataset. Other work tried to approximate it using a static analysis tools.…”

Section: B Vulnerable Entitymentioning

confidence: 99%

“…In a follow up study [13], the same authors tried to figure out whether a traditional defect prediction models, trained on complexity, code churn and past fault history is capable to predict vulnerable components. They also tried to perform training to distinguish between bugs and vulnerabilities and found out that it was hard to do so, as their results were similar for both examined cases.…”

Section: Related Workmentioning

confidence: 99%

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Vulnerability Prediction Models: A Case Study on the Linux Kernel

Jiménez

Papadakis

Traon

2016

2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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Abstract-To assist the vulnerability identification process, researchers proposed prediction models that highlight (for inspection) the most likely to be vulnerable parts of a system. In this paper we aim at making a reliable replication and comparison of the main vulnerability prediction models. Thus, we seek for determining their effectiveness, i.e., their ability to distinguish between vulnerable and non-vulnerable components, in the context of the Linux Kernel, under different scenarios. To achieve the above-mentioned aims, we mined vulnerabilities reported in the National Vulnerability Database and created a large dataset with all vulnerable components of Linux from 2005 to 2016. Based on this, we then built and evaluated the prediction models. We observe that an approach based on the header files included and on function calls performs best when aiming at future vulnerabilities, while text mining is the best technique when aiming at random instances. We also found that models based on code metrics perform poorly. We show that in the context of the Linux kernel, vulnerability prediction models can be superior to random selection and relatively precise. Thus, we conclude that practitioners have a valuable tool for prioritizing their security inspection efforts.

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Section: B Differences With Previous Studiescontrasting

confidence: 87%

Section: A Implicationsmentioning

confidence: 75%

Section: B Vulnerable Entitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Vulnerability Prediction Models: A Case Study on the Linux Kernel

Jiménez

Papadakis

Traon

2016

2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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“…However, not all of the vulnerabilities that a product contains are always reported, and therefore many components that are considered clean in the dataset may in fact be vulnerable. Moreover, the number of vulnerable files that a software product includes is often too small [35], leading to highly imbalanced datasets, which influence significantly the accuracy of the produced predictors [32]. The usage of a highly balanced and sound dataset is expected to improve the accuracy of the produced VPMs.…”

Section: Vulnerability Prediction Modelingmentioning

confidence: 99%

Static Analysis-Based Approaches for Secure Software Development

Siavvas

Gelenbe

Kehagias

et al. 2018

Communications in Computer and Information Science

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Abstract. Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.

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A performance evaluation of deep‐learnt features for software vulnerability detection

Ban

Liu

Chen

et al. 2018

Concurrency and Computation

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Summary Software vulnerability is a critical issue in the realm of cyber security. In terms of techniques, machine learning (ML) has been successfully used in many real‐world problems such as software vulnerability detection, malware detection and function recognition, for high‐quality feature representation learning. In this paper, we propose a performance evaluation study on ML based solutions for software vulnerability detection, conducting three experiments: machine learning‐based techniques for software vulnerability detection based on the scenario of single type of vulnerability and multiple types of vulnerabilities per dataset; machine learning‐based techniques for cross‐project software vulnerability detection; and software vulnerability detection when facing the class imbalance problem with varying imbalance ratios. Experimental results show that it is possible to employ software vulnerability detection based on ML techniques. However, ML‐based techniques suffer poor performance on both cross‐project and class imbalance problem in software vulnerability detection.

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Can traditional fault prediction models be used for vulnerability prediction?

Cited by 170 publications

References 35 publications

Vulnerability Prediction Models: A Case Study on the Linux Kernel

Vulnerability Prediction Models: A Case Study on the Linux Kernel

Static Analysis-Based Approaches for Secure Software Development

A performance evaluation of deep‐learnt features for software vulnerability detection

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