The Anatomy of a Vulnerability Database: A Systematic Mapping Study

Li, Xiaozhou; Moreschini, Sergio; Zhang, Zheying; Palomba, Fabio; Taibi, Davide

doi:10.2139/ssrn.4211033

Cited by 1 publication

(1 citation statement)

References 94 publications

(191 reference statements)

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“…In fact, they do refer to the usage of base units and how that constrains vulnerability detection approaches, though they do not refer to a base unit by that name. With the boom of data-driven vulnerability detection, understanding vulnerability datasets has also become important [28].…”

Section: Related Workmentioning

confidence: 99%

Toward Improved Deep Learning-based Vulnerability Detection

Sejfia,

Das,

Shafiq

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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Deep learning (DL) has been a common thread across several recent techniques for vulnerability detection. The rise of large, publicly available datasets of vulnerabilities has fueled the learning process underpinning these techniques. While these datasets help the DL-based vulnerability detectors, they also constrain these detectors' predictive abilities. Vulnerabilities in these datasets have to be represented in a certain way, e.g., code lines, functions, or program slices within which the vulnerabilities exist. We refer to this representation as a base unit. The detectors learn how base units can be vulnerable and then predict whether other base units are vulnerable. We have hypothesized that this focus on individual base units harms the ability of the detectors to properly detect those vulnerabilities that span multiple base units (or MBU vulnerabilities). For vulnerabilities such as these, a correct detection occurs when all comprising base units are detected as vulnerable. Verifying how existing techniques perform in detecting all parts of a vulnerability is important to establish their effectiveness for other downstream tasks. To evaluate our hypothesis, we conducted a study focusing on three prominent DL-based detectors: ReVeal, DeepWukong, and LineVul. Our study shows that all three detectors contain MBU vulnerabilities in their respective datasets. Further, we observed significant accuracy drops when detecting these types of vulnerabilities. We present our study and a framework that can be used to help DL-based detectors toward the proper inclusion of MBU vulnerabilities.

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Section: Related Workmentioning

confidence: 99%