Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, , 117 - 125, 28.12.2022
https://doi.org/10.46810/tdfd.1201248

Öz

Kaynakça

  • [1]Mikolov, T., Chen, K., Corrado, G., & Dean, J. Efficient Estimation of Word Representations in Vector Space.2013; arXiv. https://doi.org/10.48550/arXiv.1301.3781
  • [2] Shi, Y., Wang, Y., & Zheng, H. Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network. Energies, 2022, 15(3), 751. [3]Koutník, J., Greff, K., Gomez, F., & Schmidhuber, J. A Clockwork RNN. 2014, arXiv. https://doi.org/10.48550/arXiv.1402.3511.
  • [4]Khurma, R.A., Aljarah, I., Sharieh, A., Mirjalili, S. EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection. In: Mirjalili, S., Faris, H., Aljarah, I. (eds) Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore. 2020, https://doi.org/10.1007/978-981-32-9990-0_8.
  • [5]Özlem B. D., Canan B. Ş., Prediction of phishing websites with deep learning using WEKA environment, Avrupa Bilim ve Teknoloji Dergisi, vol. 24, pp. 35-41, 2021. doi:10.31590/ejosat.901465.
  • [6]Guha, R., Chatterjee, B., Khalid Hassan, S.K., Ahmed, S., Bhattacharyya, T., Sarkar, R. Py_FS: A Python Package for Feature Selection Using Meta-Heuristic Optimization Algorithms. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, 2022, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_42.
  • [7]Riyahi, M, Rafsanjani, MK, Gupta, BB, Alhalabi, W. Multiobjective whale optimization algorithm based feature selection for intelligent systems. Int J Intell Syst. 2022; 37: 9037- 9054. doi:10.1002/int.22979
  • [8]Abu Khurma, R.; Aljarah, I.; Sharieh, A.; Abd Elaziz, M.; Damaševičius, R.; Krilavičius, T. A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem. Mathematics 2022, 10, 464. https://doi.org/10.3390/math10030464.
  • [9]Rohlfs, C. Generalization in Neural Networks: A Broad Survey. 2022, arXiv. https://doi.org/10.48550/arXiv.2209.01610.
  • [10]Zhang, G, Ding, Z, Xu, J, Zhong, G, Jiang, N, Zhang, Y. Reasoning and tracing of information security events in the expressway networking system based on deep learning. Int J Intell Syst. 2022; 37: 8988- 9012. doi:10.1002/int.22977.
  • [11]https://nvd.nist.gov/
  • [12]https://cve.mitre.org/
  • [13]Batur Şahin, C. "Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism". Avrupa Bilim ve Teknoloji Dergisi (2022 ): 156-165. https://doi.org/10.31590/ejosat.1159875.
  • [14]C. B. Şahin, DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network, 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2021, pp. 1-8, doi: 10.1109/INISTA52262.2021.9548609.
  • [15]Tansel D., Ayça D. and Hakan K. A Comprehensive Survey on Recent Metaheuristics for Feature Selection. 2022, Neurocomputing, 494, 269-296.
  • [16]Abd Elaziz, M., Dahou, A., Abualigah, L. et al. Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Comput & Applic 33, 14079–14099 (2021). https://doi.org/10.1007/s00521-021-05960-5.
  • [17]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.
  • [18]Singh, S.K., Chaturvedi, A. Applying Deep Learning for Discovery and Analysis of Software Vulnerabilities: A Brief Survey. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, 2020, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_59.
  • [19]Dinler, Ö.B., Nizamettin A. An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection. Appl. Sci. 2020, 10, 1273. https://doi.org/10.3390/app10041273.
  • [20]Ullah, A., Aznaoui, H., Sahin, C. B, Sadie, M., Ozlem Dinler, Ö.B, Imane, L, Cloud computing and 5G challenges and open issues, (2022), 11-3, http://doi.org/10.11591/ijaas.v11.i3.pp187-193.
  • [21]Batur Şahin C. , Batur Dinler Ö. , Abualigah L. Analysis of Risk Factors in the Scope of Distributed Software Team Structure. EJOSAT. 2021; (28): 417-424.
  • [22]Batur Şahin, C., Abualigah, L. A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Comput & Applic 33, 14049–14067 (2021). https://doi.org/10.1007/s00521-021-06047-x.
  • [23]Batur Dinler Ö. , Batur Şahin C. Prediction of Phishing Web Sites with Deep Learning Using WEKA Environment. EJOSAT. 2021; (24): 35-41.
  • [24]Batur Şahin C. , Diri B. Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification. Balkan Journal of Electrical and Computer Engineering. 2021; 9(1): 23-32.
  • [25]Ullah A. , Batur Dinler Ö. , Batur Şahin C. The Effect of Technology and Service on Learning Systems During the COVID-19 Pandemic. EJOSAT. 2021; (28): 106-114.

Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms

Yıl 2022, , 117 - 125, 28.12.2022
https://doi.org/10.46810/tdfd.1201248

Öz

Yazılım güvenlik açığının tahmini, güvenli yazılım geliştirmek için önemli bir husustur. Ancak, bir bilgi sistemine saldırı yapıldığında büyük kayıplara neden olabilir. Tehlikeli kodun tespiti büyük çaba gerektirir ve bu da bilinmeyen ciddi sonuçlara yol açabilir. Etkili güvenlik sağlamak ve güvenlik açıklarının oluşmasını önlemek veya güvenlik açıklarını azaltmak için meta-sezgisel tabanlı yaklaşımlar geliştirmeye güçlü bir ihtiyaç vardır. Yazılım güvenlik açığı tahmin modelleri üzerine yapılan araştırmalar, temel olarak, güvenlik açıklarının varlığı ile ilişkili en iyi tahmin ediciler kümesini belirlemeye odaklanmıştır. Buna rağmen, mevcut güvenlik açığı algılama yöntemleri, genel özelliklere veya yerel özelliklere yönelik önyargı ve kaba algılama ayrıntı düzeyine sahiptir. Bu yazıda, önerilen çerçeve, bir saat-çalışma belleği mekanizmasına dayalı yazılım güvenlik açıkları ile ilişkili en iyi optimize edilmiş güvenlik açığı kalıpları kümesi için optimizasyon algoritmalarını geliştirmektedir. Geliştirilen algoritmanın etkinliği, LibTIFF, Pidgin, FFmpeg, LibPNG, Asteriks ve VLC medya oynatıcı veri kümeleri gibi 6 açık kaynak projesine dayanan saatli çalışan bellek mekanizması ile daha da artırılmıştır.

Kaynakça

  • [1]Mikolov, T., Chen, K., Corrado, G., & Dean, J. Efficient Estimation of Word Representations in Vector Space.2013; arXiv. https://doi.org/10.48550/arXiv.1301.3781
  • [2] Shi, Y., Wang, Y., & Zheng, H. Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network. Energies, 2022, 15(3), 751. [3]Koutník, J., Greff, K., Gomez, F., & Schmidhuber, J. A Clockwork RNN. 2014, arXiv. https://doi.org/10.48550/arXiv.1402.3511.
  • [4]Khurma, R.A., Aljarah, I., Sharieh, A., Mirjalili, S. EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection. In: Mirjalili, S., Faris, H., Aljarah, I. (eds) Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore. 2020, https://doi.org/10.1007/978-981-32-9990-0_8.
  • [5]Özlem B. D., Canan B. Ş., Prediction of phishing websites with deep learning using WEKA environment, Avrupa Bilim ve Teknoloji Dergisi, vol. 24, pp. 35-41, 2021. doi:10.31590/ejosat.901465.
  • [6]Guha, R., Chatterjee, B., Khalid Hassan, S.K., Ahmed, S., Bhattacharyya, T., Sarkar, R. Py_FS: A Python Package for Feature Selection Using Meta-Heuristic Optimization Algorithms. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, 2022, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_42.
  • [7]Riyahi, M, Rafsanjani, MK, Gupta, BB, Alhalabi, W. Multiobjective whale optimization algorithm based feature selection for intelligent systems. Int J Intell Syst. 2022; 37: 9037- 9054. doi:10.1002/int.22979
  • [8]Abu Khurma, R.; Aljarah, I.; Sharieh, A.; Abd Elaziz, M.; Damaševičius, R.; Krilavičius, T. A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem. Mathematics 2022, 10, 464. https://doi.org/10.3390/math10030464.
  • [9]Rohlfs, C. Generalization in Neural Networks: A Broad Survey. 2022, arXiv. https://doi.org/10.48550/arXiv.2209.01610.
  • [10]Zhang, G, Ding, Z, Xu, J, Zhong, G, Jiang, N, Zhang, Y. Reasoning and tracing of information security events in the expressway networking system based on deep learning. Int J Intell Syst. 2022; 37: 8988- 9012. doi:10.1002/int.22977.
  • [11]https://nvd.nist.gov/
  • [12]https://cve.mitre.org/
  • [13]Batur Şahin, C. "Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism". Avrupa Bilim ve Teknoloji Dergisi (2022 ): 156-165. https://doi.org/10.31590/ejosat.1159875.
  • [14]C. B. Şahin, DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network, 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2021, pp. 1-8, doi: 10.1109/INISTA52262.2021.9548609.
  • [15]Tansel D., Ayça D. and Hakan K. A Comprehensive Survey on Recent Metaheuristics for Feature Selection. 2022, Neurocomputing, 494, 269-296.
  • [16]Abd Elaziz, M., Dahou, A., Abualigah, L. et al. Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Comput & Applic 33, 14079–14099 (2021). https://doi.org/10.1007/s00521-021-05960-5.
  • [17]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.
  • [18]Singh, S.K., Chaturvedi, A. Applying Deep Learning for Discovery and Analysis of Software Vulnerabilities: A Brief Survey. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, 2020, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_59.
  • [19]Dinler, Ö.B., Nizamettin A. An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection. Appl. Sci. 2020, 10, 1273. https://doi.org/10.3390/app10041273.
  • [20]Ullah, A., Aznaoui, H., Sahin, C. B, Sadie, M., Ozlem Dinler, Ö.B, Imane, L, Cloud computing and 5G challenges and open issues, (2022), 11-3, http://doi.org/10.11591/ijaas.v11.i3.pp187-193.
  • [21]Batur Şahin C. , Batur Dinler Ö. , Abualigah L. Analysis of Risk Factors in the Scope of Distributed Software Team Structure. EJOSAT. 2021; (28): 417-424.
  • [22]Batur Şahin, C., Abualigah, L. A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Comput & Applic 33, 14049–14067 (2021). https://doi.org/10.1007/s00521-021-06047-x.
  • [23]Batur Dinler Ö. , Batur Şahin C. Prediction of Phishing Web Sites with Deep Learning Using WEKA Environment. EJOSAT. 2021; (24): 35-41.
  • [24]Batur Şahin C. , Diri B. Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification. Balkan Journal of Electrical and Computer Engineering. 2021; 9(1): 23-32.
  • [25]Ullah A. , Batur Dinler Ö. , Batur Şahin C. The Effect of Technology and Service on Learning Systems During the COVID-19 Pandemic. EJOSAT. 2021; (28): 106-114.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Canan Batur Şahin 0000-0002-2131-6368

Yayımlanma Tarihi 28 Aralık 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Batur Şahin, C. (2022). Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms. Türk Doğa Ve Fen Dergisi, 11(4), 117-125. https://doi.org/10.46810/tdfd.1201248
AMA Batur Şahin C. Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms. TDFD. Aralık 2022;11(4):117-125. doi:10.46810/tdfd.1201248
Chicago Batur Şahin, Canan. “Optimization of Software Vulnerabilities Patterns With the Meta-Heuristic Algorithms”. Türk Doğa Ve Fen Dergisi 11, sy. 4 (Aralık 2022): 117-25. https://doi.org/10.46810/tdfd.1201248.
EndNote Batur Şahin C (01 Aralık 2022) Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms. Türk Doğa ve Fen Dergisi 11 4 117–125.
IEEE C. Batur Şahin, “Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms”, TDFD, c. 11, sy. 4, ss. 117–125, 2022, doi: 10.46810/tdfd.1201248.
ISNAD Batur Şahin, Canan. “Optimization of Software Vulnerabilities Patterns With the Meta-Heuristic Algorithms”. Türk Doğa ve Fen Dergisi 11/4 (Aralık 2022), 117-125. https://doi.org/10.46810/tdfd.1201248.
JAMA Batur Şahin C. Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms. TDFD. 2022;11:117–125.
MLA Batur Şahin, Canan. “Optimization of Software Vulnerabilities Patterns With the Meta-Heuristic Algorithms”. Türk Doğa Ve Fen Dergisi, c. 11, sy. 4, 2022, ss. 117-25, doi:10.46810/tdfd.1201248.
Vancouver Batur Şahin C. Optimization of Software Vulnerabilities patterns with the Meta-Heuristic Algorithms. TDFD. 2022;11(4):117-25.