Article: Software vulnerability detection using SFDMN deep learning model Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2024 Vol.47 No.4 pp.227 - 239 Abstract: In recent years, various software systems have been most commonly utilised in different life and production fields. The software vulnerabilities are the major security issues that occur in software. Hence, this paper presents ShuffleFDeepMaxout network (SFDMN) for the detection of vulnerabilities in software. Initially, the source-level intermediate representation (SIR) function from the input software is extracted for the creation of semantic subgraph. Similarly, the code exposure features are extracted, where the extracted code exposure features and semantic subgraph is fed into the SFDMN for the detection of software vulnerabilities. The different deep learning models, such as deep maxout network (DMN) and ShuffleNet are integrated to design the SFDMN model for accurately detecting the vulnerabilities. Thus, the experimental outcomes proved that the SFDMN achieved superior performance by varying the training data with maximum specificity of 92.62%, sensitivity of 89.97%, and accuracy of 89.99% by using the MSR_20_Code_vulnerability_CSV_Dataset and Security-patches-dataset. Inderscience Publishers - linking academia, business and industry through research

Title: Software vulnerability detection using SFDMN deep learning model

Authors: Gayatri Bajantri; C. Noorullah Shariff

Addresses: Department of Computer Science & Engineering, BLDEA's V.P. Dr. P.G. Halakatti College of Engineering & Technology, Vijayapura Ashram Road, Vijayapur, Karnataka, 586103, India; Visvesvaraya Technological University, 'Jnana Sangama', Belagavi, 590018, Karnataka, India ' Department of AIML, Ballari Institute of Technology and Management, Ballari, Jnana Gangotri Campus, Near Allipur, Bellary, Karnataka, 583104, India

Abstract: In recent years, various software systems have been most commonly utilised in different life and production fields. The software vulnerabilities are the major security issues that occur in software. Hence, this paper presents ShuffleFDeepMaxout network (SFDMN) for the detection of vulnerabilities in software. Initially, the source-level intermediate representation (SIR) function from the input software is extracted for the creation of semantic subgraph. Similarly, the code exposure features are extracted, where the extracted code exposure features and semantic subgraph is fed into the SFDMN for the detection of software vulnerabilities. The different deep learning models, such as deep maxout network (DMN) and ShuffleNet are integrated to design the SFDMN model for accurately detecting the vulnerabilities. Thus, the experimental outcomes proved that the SFDMN achieved superior performance by varying the training data with maximum specificity of 92.62%, sensitivity of 89.97%, and accuracy of 89.99% by using the MSR_20_Code_vulnerability_CSV_Dataset and Security-patches-dataset.

Keywords: ShuffleFDeepMaxout network; SFDMN; deep maxout network; ShuffleNet; source-level intermediate representation; software vulnerability.

DOI: 10.1504/IJAHUC.2024.142719

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.4, pp.227 - 239

Received: 23 Feb 2024
Accepted: 05 Jun 2024
Published online: 18 Nov 2024 *

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Title: Software vulnerability detection using SFDMN deep learning model

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