AVDHRAM: Automated Vulnerability Detection based on Hierarchical Representation and Attention Mechanism

An, Wenyan; Chen, Liwei; Du, Gewangzi; Shi, Gang; Meng, Dan

doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00068

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…is method achieves a lower false-negative rate than other vulnerability detection methods; applying VulDeePecker to three software products (Xen, Seamonkey, and Libav), four never-reported vulnerabilities were detected. An et al proposed an automatic vulnerability detection framework (HAN) [21] based on hierarchical representation and attention mechanism, in which the framework uses five different granularity code slices according to the semantics of the source code during source code representation instead of functions, files, or…”

Section: Pattern-basedmentioning

confidence: 99%

SFN: A Novel Scalable Feature Network for Vulnerability Representation of Open-Source Codes

Guo

Wang

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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Vulnerability detection technology has become a hotspot in the field of software security, and most of the current methods do not have a complete consideration during code characterizing, which leads to problems such as information loss. Therefore, this paper proposes one class of Scalable Feature Network (SFN), a composite feature extraction method based on Continuous Bag of Words and Convolutional Neural Network. In addition, to characterize the source code more comprehensively, we construct multiscale code metrics in terms of semantic-, line-, and function granularity. In order to verify the effectiveness of the SFN, this paper builds a Scalable Vulnerability Detection Model (SVDM) by combining SFN with Bi-LSTM. The experimental results show that the proposed SVDM can obtain precision over 84.3% and recall at 83.4%, respectively, while both FNR and FPR are less than 17%.

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Section: Pattern-basedmentioning

confidence: 99%

SFN: A Novel Scalable Feature Network for Vulnerability Representation of Open-Source Codes

Guo

Wang

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…To optimally represent the characteristics of a node in the entire code attribute graph, it is necessary to map the information of its neighbor nodes to itself. Considering that each neighbor node has different weights for its influence, we use a Graph Attention Network with a multi-head attention mechanism; the aggregation effect of the attention mechanism on node information has been demonstrated in many studies [27], [34], [45], [46], [47], [48]. Besides, the attention mechanism can deal with latent noise features.…”

Section: Graph Feature Extractionmentioning

confidence: 99%

Vulnerability Detection With Graph Attention Network And Metric Learning

Zhang¹,

Liu²,

Fan³

et al. 2022

Preprint

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Static code vulnerability detection is a critical topic in software security. Existing software analysis methods have a high rate of false positives and false negatives. Researchers are interested in employing deep learning to discover vulnerabilities automatically, thanks to the recent success of deep learning algorithms in other application domains.This paper aims at the problem of insufficient and effective extraction of syntax and semantics, the issue of data imbalance, and the problem of overlapping feature distributions between vulnerable and non-vulnerable samples. We illustrate how to create models in a more principled way. We build GSM, a systematic vulnerability detection model based on Graph Attention Network, Sampling methods, and Metric Learning, one phase for one problem solution. When compared to the state-of-the-art approaches, our method achieves 11.5%, 12.3%, 12.57%, and 7.90% improvement in Precision, Recall, F1-Score, and AUC, respectively. Finally, based on the methods proposed in each stage of this paper, we put forward directions and suggestions for more efficient vulnerability detection tasks in the following research.

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Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection

Nong

Sharma

Hamou-Lhadj

et al. 2023

IIEEE Trans. Software Eng.

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AVDHRAM: Automated Vulnerability Detection based on Hierarchical Representation and Attention Mechanism

Cited by 5 publications

References 12 publications

SFN: A Novel Scalable Feature Network for Vulnerability Representation of Open-Source Codes

SFN: A Novel Scalable Feature Network for Vulnerability Representation of Open-Source Codes

Vulnerability Detection With Graph Attention Network And Metric Learning

Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection

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