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Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism

Year 2022, Issue: 41, 156 - 165, 30.11.2022
https://doi.org/10.31590/ejosat.1159875

Abstract

It is possible to better provide the security of the codebase and keep testing efforts at a minimum level by detecting vulnerable codes early in the course of software development. We assume that nature-inspired metaheuristic optimization algorithms can obtain “optimized patterns” from vulnerabilities created in an artificial manner. This study aims to use nature-inspired optimization algorithms combining heterogeneous data sources with the objective of learning optimized representations of vulnerable source codes. The chosen vulnerability-relevant data sources are cross-domain, involving historical vulnerability data from variable software projects and data from the Software Assurance Reference Database (SARD) comprising vulnerability examples. The main purpose of this paper is to outline the state-of-the-art and to analyze and discuss open challenges with regard to the most relevant areas in the field of bio-inspired optimization based on the representation of software vulnerability. Empirical research has demonstrated that the optimized representations produced by the suggested nature-inspired optimization algorithms are feasible and efficient and can be transferred for real-world vulnerability detection.

References

  • T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013.
  • Shi, Y., Wang, Y., & Zheng, H. (2022). Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network. Energies, 15(3), 751.
  • J. Koutnik, K. Greff, F. Gomez, J.Schmidhuber. "A Clockwork RNN," Proceedings of the 31st International Conference on Machine Learning, 2014,PMLR 32(2):1863-1871.
  • Khurma, R.A., Aljarah, I., Sharieh, A.A., & Mirjalili, S.M. (2019). EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection. Algorithms for Intelligent Systems.
  • Ö. B. Dinler, C. B. Şahin, “Prediction of phishing web sites with deep learning using WEKA environment,” Avrupa Bilim ve Teknoloji Dergisi, vol. 24, pp. 35-41, 2021. doi:10.31590/ejosat.901465.

Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism

Year 2022, Issue: 41, 156 - 165, 30.11.2022
https://doi.org/10.31590/ejosat.1159875

Abstract

It is possible to better provide the security of the codebase and keep testing efforts at a minimum level by detecting vulnerable codes early in the course of software development. We assume that nature-inspired metaheuristic optimization algorithms can obtain “optimized patterns” from vulnerabilities created in an artificial manner. This study aims to use nature-inspired optimization algorithms combining heterogeneous data sources with the objective of learning optimized representations of vulnerable source codes. The chosen vulnerability-relevant data sources are cross-domain, involving historical vulnerability data from variable software projects and data from the Software Assurance Reference Database (SARD) comprising vulnerability examples. The main purpose of this paper is to outline the state-of-the-art and to analyze and discuss open challenges with regard to the most relevant areas in the field of bio-inspired optimization based on the representation of software vulnerability. Empirical research has demonstrated that the optimized representations produced by the suggested nature-inspired optimization algorithms are feasible and efficient and can be transferred for real-world vulnerability detection.

References

  • T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013.
  • Shi, Y., Wang, Y., & Zheng, H. (2022). Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network. Energies, 15(3), 751.
  • J. Koutnik, K. Greff, F. Gomez, J.Schmidhuber. "A Clockwork RNN," Proceedings of the 31st International Conference on Machine Learning, 2014,PMLR 32(2):1863-1871.
  • Khurma, R.A., Aljarah, I., Sharieh, A.A., & Mirjalili, S.M. (2019). EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection. Algorithms for Intelligent Systems.
  • Ö. B. Dinler, C. B. Şahin, “Prediction of phishing web sites with deep learning using WEKA environment,” Avrupa Bilim ve Teknoloji Dergisi, vol. 24, pp. 35-41, 2021. doi:10.31590/ejosat.901465.
There are 5 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Canan Batur Şahin 0000-0002-2131-6368

Early Pub Date October 2, 2022
Publication Date November 30, 2022
Published in Issue Year 2022 Issue: 41

Cite

APA Batur Şahin, C. (2022). Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism. Avrupa Bilim Ve Teknoloji Dergisi(41), 156-165. https://doi.org/10.31590/ejosat.1159875