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A comparative study of neural network techniques for automatic software vulnerability detection
[article]
2021
pre-print
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At present, the most commonly used method for detecting software vulnerabilities is static analysis. ...
To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. ...
Section III describes the details of the automatic software vulnerability detection system using neural network techniques. ...
doi:10.1109/tase49443.2020.00010
arXiv:2104.14978v1
fatcat:ta5fpyeksfcwpdfqhuw6pdsgwa
Software Vulnerability Analysis and Discovery using Deep Learning Techniques: A Survey
2020
IEEE Access
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The focus is on how to use the emerging neural network techniques for capturing potentially vulnerable code patterns. ...
MISCELLANEOUS A CNN-based approach is proposed in [18] to detect software vulnerability automatically. ...
doi:10.1109/access.2020.3034766
fatcat:3fpbunyedza2ree3ozle6o63ce
A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques
2020
Security and Communication Networks
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The development of deep learning technology has brought new opportunities for the study of potential security issues in software, and researchers have successively proposed many automation methods. ...
vulnerability detection, automated program repair, and automated defect prediction. ...
Software fault location is a prerequisite for automatic program repair and is mainly used to identify the location of potential defects or vulnerabilities in the program. ...
doi:10.1155/2020/8858010
fatcat:obeiw4p7afan5m24ydmdkmyhbm
Software Vulnerability Classification Based on Deep Neural Network
2019
International Journal of Engineering and Advanced Technology
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The system carried software vulnerability detection based on the Deep Neural Network (DNN). a new dynamic vulnerability classification approach has suggested. ...
the re-factor possibility in entire code while in third section we build DNN module for software vulnerability detection and finally recommend the vulnerability for entire code. ...
The system provided an overview of the static analysis tools and techniques and subsequently detailed the proposed vulnerability detection based on the deep neural network. ...
doi:10.35940/ijeat.a9746.109119
fatcat:i6dpwkpapfhnhe6fmnkpd3lo4e
Deep Learning for Software Vulnerabilities Detection Using Code Metrics
2020
IEEE Access
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Software vulnerability can cause disastrous consequences for information security. Earlier detection of vulnerabilities minimizes these consequences. ...
INDEX TERMS Automatic vulnerability prediction, code metrics, deep neural networks. 74562 This work is licensed under a Creative Commons Attribution 4.0 License. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their insightful comments and suggestions that helped us improve the article. ...
doi:10.1109/access.2020.2988557
fatcat:rgfabzeerbfwxgacwunwwg3lay
Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery
2021
International Journal of Advanced Trends in Computer Science and Engineering
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ML techniques that can professionally handle these attacks and we expect the net result of this survey article to help indesigning the new detection model for identifying the above-mentioned attacks ...
Today's age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both techniques play a significant role in measuring vulnerability prediction accuracy. ...
The study results are a promising approach for automated software vulnerability detection. [10] Has studied that the fuzzing techniques are to be used for finding bugs by executing the software with a ...
doi:10.30534/ijatcse/2021/871032021
fatcat:fhx2y72a5fdadmj2yyqqjnj37i
An Automatic Source Code Vulnerability Detection Approach Based on KELM
2021
Security and Communication Networks
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Bidirectional Long Short-term Memory (Bi-LSTM) network has proved a success for software vulnerability detection. ...
To mitigate this issue, researchers introduced neural networks to automatically extract features to improve the intelligence of vulnerability detection. ...
Section 2 discusses the work related to automatic detection of software vulnerability. Section 3 describes the details of the proposed automatic software vulnerability detection method. ...
doi:10.1155/2021/5566423
fatcat:d4ux3oawjfgfvdxtjze7c5q62i
DeeDP: vulnerability detection and patching based on deep learning
2020
Theoretical and Applied Cybersecurity
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We present the DeeDP system for automatic vulnerabilities detection and patch providing. DeeDP allows to detect vulnerabilities in C/C++ source code and generate patch for fixing detected issue. ...
This system uses deep learning methods to organize rules for deciding whether a code fragment is vulnerable. ...
Introduction There are many cyber attacks which are rooted in software vulnerabilities. Prevention of software products compromising is related to application of different techniques e.g. ...
doi:10.20535/tacs.2664-29132020.1.209465
fatcat:riq4wyejcneithoqdh3w7pujem
MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities
[article]
2022
arXiv
pre-print
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As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. ...
Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. ...
RELATED WORKS
A. Graph Embedding Neural Networks A few studies have detected smart contract vulnerabilities using neural network-based embedding techniques. Zhuang et al. ...
arXiv:2208.13252v2
fatcat:hv7w4pcyujegdj7rbjoj5fcewq
Deep ahead-of-threat virtual patching
[article]
2020
arXiv
pre-print
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Security solutions provide defenses against these attacks through continuous application testing, fast-patching of vulnerabilities, automatic deployment of patches, and virtual patching detection techniques ...
We leverage testing techniques for supervised-learning data generation, and show how artificial intelligence techniques can use this data to create predictive deep neural-network models that read an application's ...
We would like to thank Ayman Jarrous and Tamer Salman for fruitful discussions, and Ben Liderman for help in building the automated framework. ...
arXiv:2007.08296v1
fatcat:slnx7lpjjvdwlh4bnpvvsqvtzu
A Survey of Software Clone Detection from Security Perspective
2021
IEEE Access
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A thorough survey of code clone identification/detection from the security perspective is indispensable for providing a comprehensive review of previous related studies and proposing potential research ...
Researchers have been studying code clone detection issues for a long time, and the discussion mainly focuses on software engineering management and system maintenance. ...
Yujie Gu and all the reviewers for their advice on this manuscript. We thank Dr. Maxine Garcia from Edanz Group (https://en-author-services.edanz.com/ac) for editing a draft of this manuscript. ...
doi:10.1109/access.2021.3065872
fatcat:fh6ysdrcqvawpay6s767i3na2u
A Modified Maximal Divergence Sequential Auto-Encoder And Time Delay Neural Network Models For Vulnerable Binary Codes Detection
2020
IEEE Access
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To the best of our knowledge, [23] is the only work has studied the use of automatically extracted features for binary code vulnerability detection. ...
(VAE) and a new regularization technique for binary code vulnerability detection. ...
doi:10.1109/access.2020.2965726
fatcat:6tq6ktwpkjgd7ej2hqtdbneova
A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
2021
Algorithms
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In this paper, we evaluate the performance of mainstream word embedding techniques in the scenario of software vulnerability detection. ...
Most ML techniques aim to isolate a unit of source code, be it a line or a function, as being vulnerable. ...
The framework is a deep neural network and is designed for the scenario where there are some identified vulnerable data available for a software project. ...
doi:10.3390/a14110335
fatcat:ucs65dzz6bgndddk62lrcxxxgm
MANDO-GURU: vulnerability detection for smart contract source code by heterogeneous graph embeddings
2022
Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Using a combination of control-ow graphs and call graphs of Solidity code, we design new heterogeneous graph attention neural networks to encode more structural and potentially semantic relations among ...
Our validation of real-world smart contract datasets shows that MANDOGURU can signicantly improve many other vulnerability detection techniques by up to 24% in terms of the F1-score at the contract level ...
In this paper, we propose a new tool with a new method for representing smart contracts as specialized graphs and learning their patterns automatically via graph neural networks on a large scale to detect ...
doi:10.1145/3540250.3558927
fatcat:g4ygznurzjddve5i2fj7ria2a4
Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing
[article]
2023
arXiv
pre-print
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We created an efficient and scalable vulnerability detection method based on a deep neural network model Long Short Term Memory (LSTM), and quantum machine learning model Long Short Term Memory (QLSTM) ...
One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. ...
arXiv:2303.07525v1
fatcat:dxhhtix7lfh3zhmx6zlijptvxm
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