Cross-platform software vulnerability detection using Vulnerascope-X with Word2Vec Node2Vec and RCGO optimized SVM
- 1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education
- 2Department of Computer Science and Engineering, B.M.S. College of Engineering
- 3Professor,AIML,IPR Cell, Nitte Meenakshi Institute of Technology, Nitte Deemed to Be University
Abstract
To increase program dependability and reduce maintenance expenses, software defect prediction is essential. For software vulnerability study across platforms, this paper presents a state-of-the-art methodology that makes use of a unique synthetic dataset called VulneraScope-X. Utilising synthetic CVE intelligence, this dataset incorporates a wealth of static, syntactic, and semantic information. This work employs an array of pre-processing methods, such as min-max normalisation, Word2Vec, Node2Vec, Synthetic Minority Oversampling Technique (SMOTE) for class balance, to deal with complexity and diversity of the features. The recently suggested Running City Game Optimiser (RCGO) outperformed state-of-the-art metaheuristics in feature selection while simultaneously lowering dimensionality and keeping predictive characteristics. With help of the features that were chosen, a Support Vector Machine (SVM) classifier was trained. The hyperparameters of this classifier were adjusted using Grid Search. Outperforming more conventional classifiers like Naive Bayes, MLP, and KNN, the model produced remarkable results with a 94.25% accuracy rate, 93.90% precision rate, 94.10% recall rate, and AUC-ROC of 0.962. In terms of accuracy and execution time, the RCGO algorithm outperformed other optimisation algorithms such as GA, GWO, CRO, and BWO. This scheme provides reproducible and scalable methodologies for software security evaluation in addition to demonstrating a high-performing pipeline for defect prediction. According to the findings, VulneraScope-X greatly improve cross-platform defect detection when combined with topological and semantic embeddings. When applied to large-scale, heterogeneous software organizations, this method demonstrates promise for vulnerability triaging and safe software development.
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BibTeX
@article{vijayamahantesh2026crossplatform,
title = {{Cross-platform software vulnerability detection using Vulnerascope-X with Word2Vec Node2Vec and RCGO optimized SVM}},
author = {Vijayamahantesh and A. Ashwitha and E. Naresh and Piyush Kumar Pareek},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63099-6},
url = {https://doi.org/10.1038/s41598-026-63099-6},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cross-platform software vulnerability detection using Vulnerascope-X with Word2Vec Node2Vec and RCGO optimized SVM
AU - Vijayamahantesh
AU - A. Ashwitha
AU - E. Naresh
AU - Piyush Kumar Pareek
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63099-6
UR - https://doi.org/10.1038/s41598-026-63099-6
AB - To increase program dependability and reduce maintenance expenses, software defect prediction is essential. For software vulnerability study across platforms, this paper presents a state-of-the-art methodology that makes use of a unique synthetic dataset called VulneraScope-X. Utilising synthetic CVE intelligence, this dataset incorporates a wealth of static, syntactic, and semantic information. This work employs an array of pre-processing methods, such as min-max normalisation, Word2Vec, Node2Vec, Synthetic Minority Oversampling Technique (SMOTE) for class balance, to deal with complexity and diversity of the features. The recently suggested Running City Game Optimiser (RCGO) outperformed state-of-the-art metaheuristics in feature selection while simultaneously lowering dimensionality and keeping predictive characteristics. With help of the features that were chosen, a Support Vector Machine (SVM) classifier was trained. The hyperparameters of this classifier were adjusted using Grid Search. Outperforming more conventional classifiers like Naive Bayes, MLP, and KNN, the model produced remarkable results with a 94.25% accuracy rate, 93.90% precision rate, 94.10% recall rate, and AUC-ROC of 0.962. In terms of accuracy and execution time, the RCGO algorithm outperformed other optimisation algorithms such as GA, GWO, CRO, and BWO. This scheme provides reproducible and scalable methodologies for software security evaluation in addition to demonstrating a high-performing pipeline for defect prediction. According to the findings, VulneraScope-X greatly improve cross-platform defect detection when combined with topological and semantic embeddings. When applied to large-scale, heterogeneous software organizations, this method demonstrates promise for vulnerability triaging and safe software development.
SN - 2045-2322
ER -