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Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

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Abstract

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

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References

  1. Catal C et al. (2017) Development of a software vulnerability prediction web service based on artificial neural networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer

  2. Hassib EM, el-Desouky AI, Labib LM, el-kenawy ESM (2020) WOA+ BRNN: an imbalanced big data classification framework using whale optimization and deep neural network. Soft Comput 24(8):5573–5592

    Article  Google Scholar 

  3. Pang Y, Xue X, Wang H (2017) Predicting vulnerable software components through deep neural network. in Proceedings of the 2017 International Conference on Deep Learning Technologies

  4. Stuckman J, Walden J, Scandariato R (2016) The effect of dimensionality reduction on software vulnerability prediction models. IEEE Trans Reliab 66(1):17–37

    Article  Google Scholar 

  5. Russell R et al. (2018) Automated vulnerability detection in source code using deep representation learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE

  6. Li Z, Zou D, Tang J, Zhang Z, Sun M, Jin H (2019) A comparative study of deep learning-based vulnerability detection system. IEEE Access 7:103184–103197

    Article  Google Scholar 

  7. Stepanov L et al. (2018) Approach to estimation of level of information security at enterprise based on genetic algorithm. in Journal of Physics: Conference Series

  8. Arasteh B, Bouyer A, Pirahesh S (2015) An efficient vulnerability-driven method for hardening a program against soft-error using genetic algorithm. Comput Electric Eng 48:25–43

    Article  Google Scholar 

  9. Kudjo PK, Chen J (2019) A cost-effective strategy for software vulnerability prediction based on bellwether analysis. in Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis

  10. Ban X et al (2019, e5103) A performance evaluation of deep-learnt features for software vulnerability detection. Concurrency Comput Practice Exp 31(19)

  11. Abualigah LM, Khader AT, al-Betar MA, Alomari OA (2017) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36

    Article  Google Scholar 

  12. Li Z, Zou D, Xu S, Jin H, Zhu Y, Chen Z (2018) SySeVR: A framework for using deep learning to detect software vulnerabilities, arXiv:1807.06756. [Online]. Available: https://arxiv.org/abs/1807.06756

  13. Russell R, Kim L, Hamilton L, Lazovich T, Harer J, Ozdemir O, Ellingwood P, McConley M (2018) Automated vulnerability detection in source code using deep representation learning, in Proc. 17th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), 757–762

  14. Dam HK et al., Automatic feature learning for predicting vulnerable software components, IEEEIEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2018.2881961, pp: (99):1- 1,2011,2011,2018

  15. Liu S et. al. (2020) CD-VulD: Cross-Domain Vulnerability Discovery based on Deep Domain AdaptAdaptation, IEEE Transactions on Dependable and Secure Computing, i:10.1. https://doi.org/10.1109/TDSC.2020.2984505. (99): 1–1

  16. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer

  17. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

    Google Scholar 

  18. Abualigah L et al (2020) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  Google Scholar 

  19. Mirjalili S (2019) Evolutionary algorithms and neural networks. Studies in Computational Intelligence

  20. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  21. Shin Y, Williams L (2013) Can traditional fault prediction models be used for vulnerability prediction? Empir Softw Eng 18(1):25–59

    Article  Google Scholar 

  22. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

  23. Phan AV, Le Nguyen M, Bui LT (2017) Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Appl Intell 46(2):455–469

    Article  Google Scholar 

  24. Benmessahel I, Xie K, Chellal M (2018) A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Appl Intell 48(8):2315–2327

    Article  Google Scholar 

  25. Safaldin M, Otair M, Abualigah L (2020) Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J Ambient Intell Humaniz Comput, 1–18

  26. Graves A, Mohamed A-r, Hinton G (2013) Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing. IEEE

  27. Zia T, Zahid U (2019) Long short-term memory recurrent neural network architectures for Urdu acoustic modeling. Int J Speech Technol 22(1):21–30

    Article  Google Scholar 

  28. Cheng Y et al (2018) Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Indust Inf 15(2):987–997

    Article  Google Scholar 

  29. Chung J et al. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  30. Brusaferri A, Matteucci M, Spinelli S, Vitali A (2020) Learning behavioral models by recurrent neural networks with discrete latent representations with application to a flexible industrial conveyor. Comput Ind 122:103263

    Article  Google Scholar 

  31. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  32. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  33. Elmasry W, Akbulut A, Zaim AH (2020) Evolving deep learning architectures for network intrintrusion detection using a double PSO metaheuristic, Computer Networks, V 168. https://doi.org/10.1016/j.comnet.2019.107042

  34. Yang J, Ye Z, Yan L, Gu W, Wang R (2018) Modified Naive Bayes Algorithm for Network IntrIntrusion Detection based on Artificial Bee Colony Algorithm, 1770 in: 2018 IEEE 4th InteInternational Symposium on Wireless Systems within the International Conferences on InteIntelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), IEEE, 35–40

  35. Khorram T, Baykan N (2018) Feature selection in network intrusion detection using metaheuristic algalgorithms, International Journal of Advance Research, Ideas and Innovations in Technology, 20 V.4, 704–710

  36. Xiang C (2018) Network intrusion detection by using particle swarm optimization and neural ne network. J Netw Technol 9(1):22–30

    Google Scholar 

  37. Arivudainambi D, Varun Kumar KA, Sibi CS (2019) Lion ids: a meta-heuristics approach to detect ddos attacks against software-defned networks. Neural Comput Appl 31(5):1491–1501

    Article  Google Scholar 

  38. Xu H., Cao Q., Fang C., Fu Y., Su J., Wei S., Bykovyy P., (2018) Application of elephant herd optimization algorithm based on levy fight strategy in intrusion detection. In: 2018 IEEE 4th international symposium on wireless systems within the international conferences on intelligent data acquisition and advanced computing systems (IDAACS-SWS), IEEE, pp 16–20

  39. Haghnegahdar L, Wang Y (2019) A whale optimization algorithm trained artificial neural network for smart grid cyber intrusion detection. In: Neural computing and applications, 1–15

  40. Khare N, Devan P, Chowdhary CL, Bhattacharya S, Singh G, Singh S, Yoon B (2020) SMO-DNN: spider monkey optimization and deep neural network hybrid classifier model for intrusion detection. Electron 9:692. https://doi.org/10.3390/electronics9040692

    Article  Google Scholar 

  41. Almomani O (2020) A feature selection model for network intrusion detection system based on PSO, GWO. FFA and GA Algorithms Symmetry 12:1046. https://doi.org/10.3390/sym12061046

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey

    Canan Batur Şahin

  2. Faculty of Computer Engineering, Siirt University, 56100, Siirt, Turkey

    Özlem Batur Dinler

  3. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

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  1. Canan Batur Şahin
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  2. Özlem Batur Dinler
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  3. Laith Abualigah
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Correspondence to Canan Batur Şahin.

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Şahin, C.B., Dinler, Ö.B. & Abualigah, L. Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Appl Intell 51, 8271–8287 (2021). https://doi.org/10.1007/s10489-021-02324-3

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