BibTex RIS Cite

Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu

Year 2018, Volume: 33 Issue: 1, 0 - 0, 08.03.2018

Atilla Özgür , Hamit Erdem

https://doi.org/10.17341/gazimmfd.406781
Cited By: 4

Abstract

Bilişim sistemlerinin gelişmesiyle, saldırı tespit sistemlerinin (STS) kullanımı önem kazanmıştır. Bu sistemlerin çalışması, genellikle sınıflandırma problemi çerçevesinde değerlendirilebilir. Sınıflandırıcı uygulamalarının en önemli aşamalardan birisi nitelik seçme aşamasıdır. Günümüzde, sınıflandırıcı başarısını artırmak için, tek sınıflandırıcı yerine sınıflandırıcı füzyonu kullanımı önerilmektedir. Bu öneride; saldırı tespit sınıflandırma uygulamalarında, nitelik seçme ve sınıflandırıcı füzyonu ağırlık belirleme işlemlerinin, genetik algoritma (GA) kullanılarak yapılması önerilmektedir. Bu sisteme, Genetik Algoritma tabanlı Nitelik Seçme ve Ağırlık Bulma (GA-NS-AB) adı verilmiştir. GA-NS-AB, saldırı tespit sistemi NSL-KDD veri kümesi üzerinde uygulanmıştır. Çoklu sınıflandırıcı füzyonunda sınıflandırıcı sayısının 3 ile 7 arasında olduğu doğrusal ağırlıklı birleştirme yöntemi kullanılmıştır. Kullanılan sınıflandırıcılar şunlardır: Adaboost, Karar Ağacı, Lojistik Regresyon, Saf Bayes, Rastgele Orman, Gradient Boosting, En yakın K komşu. Önerilen yöntem, GA-NS-AB, diğer füzyon yöntemleri ( basit ve olasılık oy) ve tek sınıflandırıcı sonuçları ile karşılaştırılmıştır. GA-NS-AB ile eğitim ve test süresi azaltılarak, doğruluk oranı değerleri daha yüksek olan bir sınıflandırıcı füzyonu elde edilmiştir.

Keywords

Nitelik Seçme Sınıflandırıcı Füzyonu; Genetik Algoritma; Saldırı Tespit Sistemleri; Makine Öğrenmesi

References

  • KAYNAKLAR (REFERENCES)
  • K. Scarfone ve P. Mell, Guide to intrusion detection and prevention systems (IDPS), vol: 800, NIST, 2007
  • S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh ve A. Kannan, Intelligent feature selection and classification techniques for intrusion detection in networks: a survey, EURASIP Journal on Wireless Communications and Networking, vol: 2013, no. 1, 2013.
  • C. Kolias, G. Kambourakis ve M. Maragoudakis, Swarm Intelligence in Intrusion Detection: A Survey, Computers and Security, vol: 30, no. 8, 625-642, 2011.
  • A. Özgür ve H. Erdem, A Review of KDD99 Dataset Usage in Intrusion Detection and Machine Learning between 2010 and 2015, PeerJ Preprints 4:e1954v1, 2016.
  • I. Guyon ve A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol: 3, 1157-1182, 2003.
  • O. Yıldız, M. Tez, H. Ş. Bilge, M. A. Akcayol ve İ. Güler, Meme Kanseri̇ Sınıflandırması İçi̇n Veri̇ Füzyonu Ve Geneti̇k Algori̇tma Tabanlı Gen Seçi̇mi̇, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 27, no. 3, 2012.
  • J. Pérez-Rodríguez, A. G. Arroyo-Peña ve N. García-Pedrajas, Simultaneous instance and feature selection and weighting using evolutionary computation: Proposal and study, Applied Soft Computing , vol: 37, 416-443, 2015.
  • Ş. Sağıroğlu, E. N. Yolaçan ve U. Yavanoğlu, Zeki Saldırı Tespit Sistemi Tasarımı Ve Gerçekleştirilmesi, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 26, no. 2, 325-340, 2011.
  • T. Tuncer ve Y. Tatar, FPGA Tabanlı Programlanabi̇li̇r Gömülü Saldırı Tespi̇t Si̇stemi̇ni̇n Gerçekleşti̇ri̇lmesi̇, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 27, no. 1, 2012.
  • T. Bass, Intrusion Detection Systems and Multisensor Data Fusion, Commun. ACM, vol: 43, no. 4, 99-105, 2000.
  • L. I. Kuncheva, J. C. Bezdek ve R. P. Duin, Decision templates for multiple classifier fusion: an experimental comparison, Pattern Recognition , vol: 34, no. 2, 299-314, 2001.
  • Y. Wang, H. Yang, X. Wang ve R. Zhang, Distributed intrusion detection system based on data fusion method, Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, 2004.
  • Y. Zhang, H. Zhang, J. Cai ve B. Yang, A Weighted Voting Classifier Based on Differential Evolution, Abstract and Applied Analysis, vol: 2014, p. 6, 2014.
  • J. Sylvester ve N. V. Chawla, Evolutionary Ensemble Creation and Thinning, %1 içinde The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006.
  • Y. Maghsoudi, A. A. , M. V. Zoej ve B. Mojaradi, Weighted Combination Of Multiple Classifiers For The classification Of Hyperspectral Images Using A Genetic algorithm, ISPRS Commission I Symposium "From Sensors to Imagery", 2006.
  • M. Dener, M. A. Akcayol, S. Toklu ve Ö. Bay, Zamana Bağlı Di̇nami̇k En Kısa Yol Problemi̇ İçi̇n Geneti̇k Algori̇tma Tabanlı Yeni̇ Bi̇r Algori̇tma, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 26, no. 4, 2013.
  • M. Üstündağ, E. Avcı, M. Gökbulut ve F. Ata, Dalgacık Paket Dönüşümü Ve Geneti̇k Algori̇tma Kullanarak Zayıf Radar Si̇nyalleri̇ni̇n Gürültüden Arındırılması, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 29, no. 2, 2014.
  • B. Gürsu, Ceza Fonksi̇yonuyla Durdurmalı Geneti̇k Algori̇tmalar İle Transformatör Merkezleri̇nde Opti̇mum Aşırı Akim Röle Koordi̇nasyonu, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 29, no. 4, 2014.
  • C. Kruegel, F. Valeur, G. Vigna ve R. Kemmerer, Stateful intrusion detection for high-speed network's, Security and Privacy, 2002. Proceedings. 2002 IEEE Symposium on, 2002.
  • C. A. Catania ve C. G. Garino, Automatic network intrusion detection: Current techniques and open issues, Computers & Electrical Engineering , vol: 38, no. 5, 1062-1072, 2012.
  • N. Hubballi ve V. Suryanarayanan, False alarm minimization techniques in signature-based intrusion detection systems: A survey, Computer Communications , vol: 49, 1-17, 2014.
  • R. K. Cunningham, R. P. Lippmann, D. J. Fried, S. L. Garfinkel, I. Graf, K. R. a. W. S. E. Kendall, D. Wyschogrod ve M. A. Zissman, Evaluating intrusion detection systems without attacking your friends: The 1998 DARPA intrusion detection evaluation, 1999.
  • W. Lee ve S. J. Stolfo, A framework for constructing features and models for intrusion detection systems, ACM Transactions on Information and System Security, vol: 3, 227-261, 2000.
  • M. Tavallaee, E. Bagheri, W. Lu ve A. A. Ghorbani, A detailed analysis of the KDD CUP 99 data set, Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications, Piscataway, NJ, USA, 2009.
  • NSL-KDD, Download Link of NSL-KDD in Github, 2016. Available: https://github.com/ati-ozgur/NSL_KDD. Yayın Tarihi Ocak 17, 2017. Erişim tarihi Ocak 15, 2018.
  • R. Sommer ve V. Paxson, Outside the Closed World: On Using Machine Learning for Network Intrusion Detection, Proceedings of the 2010 IEEE Symposium on Security and Privacy, Washington, DC, USA, 2010.
  • S. Brugger, KDD Cup 99 dataset (Network Intrusion) considered harmful, vol: 7, 2007, p. 15.
  • L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, Wiley-Interscience, 2004.
  • A. Kalınlı ve Ö. Aksu, Baskın Gen Seçi̇mi̇ Operatörüne Dayalı Geneti̇k Algori̇tma Modeli̇, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 26, no. 4, 2013.
  • T. Bäck, Optimal Mutation Rates in Genetic Search, Proceedings of the 5th International Conference on Genetic Algorithms, San Francisco, CA, USA, 1993.
  • L. I. a. W. C. J. Kuncheva, Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy, Machine Learning, vol: 51, no. 2, 181-207, 2003.
  • S.-H. Kang ve K. J. Kim, A feature selection approach to find optimal feature subsets for the network intrusion detection system, Cluster Computing, vol: 19, 325-333, 2016.
  • C. R. Pereira, R. Y. M. Nakamura, K. A. P. Costa ve J. P. Papa, An Optimum-Path Forest framework for intrusion detection in computer networks, Engineering Applications of Artificial Intelligence, vol: 25, 1226-1234, 2012.
  • S. Rastegari, P. Hingston ve C.-P. Lam, Evolving statistical rulesets for network intrusion detection, Applied Soft Computing, vol: 33, 348-359, 2015.
  • N. A. Seresht ve R. Azmi, MAIS-IDS: A distributed intrusion detection system using multi-agent AIS approach, Engineering Applications of Artificial Intelligence, vol: 35, 286-298, 2014.
  • D. M. Farid, L. Zhang, C. M. Rahman, M. A. Hossain ve R. Strachan, Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks, Expert Systems with Applications, vol: 41, 1937-1946, 2014.
  • R. Singh, H. Kumar ve R. K. Singla, An intrusion detection system using network traffic profiling and online sequential extreme learning machine, Expert Systems with Applications, vol: 42, 8609-8624, 2015.
  • S. Bhattacharya ve S. Selvakumar, LAWRA: a layered wrapper feature selection approach for network attack detection, Security and Communication Networks, vol: 8, 3459-3468, 2015.
  • M. Mohammadi, B. Raahemi, A. Akbari ve B. Nassersharif, New class-dependent feature transformation for intrusion detection systems, Security and Communication Networks, vol: 5, 1296-1311, 2012.
  • Q. Liu, J. Yin, V. C. M. Leung, J.-H. Zhai, Z. Cai ve J. Lin, Applying a new localized generalization error model to design neural networks trained with extreme learning machine, Neural Computing and Applications, vol: 27, 59-66, 2016.
  • E. D. la Hoz, E. D. L. Hoz, A. Ortiz, J. Ortega ve B. Prieto, PCA filtering and probabilistic SOM for network intrusion detection, Neurocomputing, vol: 164, 71-81, 2015.

Year 2018, Volume: 33 Issue: 1, 0 - 0, 08.03.2018

Atilla Özgür , Hamit Erdem

https://doi.org/10.17341/gazimmfd.406781
Cited By: 4

Abstract

References

  • KAYNAKLAR (REFERENCES)
  • K. Scarfone ve P. Mell, Guide to intrusion detection and prevention systems (IDPS), vol: 800, NIST, 2007
  • S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh ve A. Kannan, Intelligent feature selection and classification techniques for intrusion detection in networks: a survey, EURASIP Journal on Wireless Communications and Networking, vol: 2013, no. 1, 2013.
  • C. Kolias, G. Kambourakis ve M. Maragoudakis, Swarm Intelligence in Intrusion Detection: A Survey, Computers and Security, vol: 30, no. 8, 625-642, 2011.
  • A. Özgür ve H. Erdem, A Review of KDD99 Dataset Usage in Intrusion Detection and Machine Learning between 2010 and 2015, PeerJ Preprints 4:e1954v1, 2016.
  • I. Guyon ve A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol: 3, 1157-1182, 2003.
  • O. Yıldız, M. Tez, H. Ş. Bilge, M. A. Akcayol ve İ. Güler, Meme Kanseri̇ Sınıflandırması İçi̇n Veri̇ Füzyonu Ve Geneti̇k Algori̇tma Tabanlı Gen Seçi̇mi̇, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 27, no. 3, 2012.
  • J. Pérez-Rodríguez, A. G. Arroyo-Peña ve N. García-Pedrajas, Simultaneous instance and feature selection and weighting using evolutionary computation: Proposal and study, Applied Soft Computing , vol: 37, 416-443, 2015.
  • Ş. Sağıroğlu, E. N. Yolaçan ve U. Yavanoğlu, Zeki Saldırı Tespit Sistemi Tasarımı Ve Gerçekleştirilmesi, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 26, no. 2, 325-340, 2011.
  • T. Tuncer ve Y. Tatar, FPGA Tabanlı Programlanabi̇li̇r Gömülü Saldırı Tespi̇t Si̇stemi̇ni̇n Gerçekleşti̇ri̇lmesi̇, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 27, no. 1, 2012.
  • T. Bass, Intrusion Detection Systems and Multisensor Data Fusion, Commun. ACM, vol: 43, no. 4, 99-105, 2000.
  • L. I. Kuncheva, J. C. Bezdek ve R. P. Duin, Decision templates for multiple classifier fusion: an experimental comparison, Pattern Recognition , vol: 34, no. 2, 299-314, 2001.
  • Y. Wang, H. Yang, X. Wang ve R. Zhang, Distributed intrusion detection system based on data fusion method, Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, 2004.
  • Y. Zhang, H. Zhang, J. Cai ve B. Yang, A Weighted Voting Classifier Based on Differential Evolution, Abstract and Applied Analysis, vol: 2014, p. 6, 2014.
  • J. Sylvester ve N. V. Chawla, Evolutionary Ensemble Creation and Thinning, %1 içinde The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006.
  • Y. Maghsoudi, A. A. , M. V. Zoej ve B. Mojaradi, Weighted Combination Of Multiple Classifiers For The classification Of Hyperspectral Images Using A Genetic algorithm, ISPRS Commission I Symposium "From Sensors to Imagery", 2006.
  • M. Dener, M. A. Akcayol, S. Toklu ve Ö. Bay, Zamana Bağlı Di̇nami̇k En Kısa Yol Problemi̇ İçi̇n Geneti̇k Algori̇tma Tabanlı Yeni̇ Bi̇r Algori̇tma, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 26, no. 4, 2013.
  • M. Üstündağ, E. Avcı, M. Gökbulut ve F. Ata, Dalgacık Paket Dönüşümü Ve Geneti̇k Algori̇tma Kullanarak Zayıf Radar Si̇nyalleri̇ni̇n Gürültüden Arındırılması, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 29, no. 2, 2014.
  • B. Gürsu, Ceza Fonksi̇yonuyla Durdurmalı Geneti̇k Algori̇tmalar İle Transformatör Merkezleri̇nde Opti̇mum Aşırı Akim Röle Koordi̇nasyonu, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 29, no. 4, 2014.
  • C. Kruegel, F. Valeur, G. Vigna ve R. Kemmerer, Stateful intrusion detection for high-speed network's, Security and Privacy, 2002. Proceedings. 2002 IEEE Symposium on, 2002.
  • C. A. Catania ve C. G. Garino, Automatic network intrusion detection: Current techniques and open issues, Computers & Electrical Engineering , vol: 38, no. 5, 1062-1072, 2012.
  • N. Hubballi ve V. Suryanarayanan, False alarm minimization techniques in signature-based intrusion detection systems: A survey, Computer Communications , vol: 49, 1-17, 2014.
  • R. K. Cunningham, R. P. Lippmann, D. J. Fried, S. L. Garfinkel, I. Graf, K. R. a. W. S. E. Kendall, D. Wyschogrod ve M. A. Zissman, Evaluating intrusion detection systems without attacking your friends: The 1998 DARPA intrusion detection evaluation, 1999.
  • W. Lee ve S. J. Stolfo, A framework for constructing features and models for intrusion detection systems, ACM Transactions on Information and System Security, vol: 3, 227-261, 2000.
  • M. Tavallaee, E. Bagheri, W. Lu ve A. A. Ghorbani, A detailed analysis of the KDD CUP 99 data set, Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications, Piscataway, NJ, USA, 2009.
  • NSL-KDD, Download Link of NSL-KDD in Github, 2016. Available: https://github.com/ati-ozgur/NSL_KDD. Yayın Tarihi Ocak 17, 2017. Erişim tarihi Ocak 15, 2018.
  • R. Sommer ve V. Paxson, Outside the Closed World: On Using Machine Learning for Network Intrusion Detection, Proceedings of the 2010 IEEE Symposium on Security and Privacy, Washington, DC, USA, 2010.
  • S. Brugger, KDD Cup 99 dataset (Network Intrusion) considered harmful, vol: 7, 2007, p. 15.
  • L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, Wiley-Interscience, 2004.
  • A. Kalınlı ve Ö. Aksu, Baskın Gen Seçi̇mi̇ Operatörüne Dayalı Geneti̇k Algori̇tma Modeli̇, Journal of the Faculty of Engineering and Architecture of Gazi University, vol: 26, no. 4, 2013.
  • T. Bäck, Optimal Mutation Rates in Genetic Search, Proceedings of the 5th International Conference on Genetic Algorithms, San Francisco, CA, USA, 1993.
  • L. I. a. W. C. J. Kuncheva, Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy, Machine Learning, vol: 51, no. 2, 181-207, 2003.
  • S.-H. Kang ve K. J. Kim, A feature selection approach to find optimal feature subsets for the network intrusion detection system, Cluster Computing, vol: 19, 325-333, 2016.
  • C. R. Pereira, R. Y. M. Nakamura, K. A. P. Costa ve J. P. Papa, An Optimum-Path Forest framework for intrusion detection in computer networks, Engineering Applications of Artificial Intelligence, vol: 25, 1226-1234, 2012.
  • S. Rastegari, P. Hingston ve C.-P. Lam, Evolving statistical rulesets for network intrusion detection, Applied Soft Computing, vol: 33, 348-359, 2015.
  • N. A. Seresht ve R. Azmi, MAIS-IDS: A distributed intrusion detection system using multi-agent AIS approach, Engineering Applications of Artificial Intelligence, vol: 35, 286-298, 2014.
  • D. M. Farid, L. Zhang, C. M. Rahman, M. A. Hossain ve R. Strachan, Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks, Expert Systems with Applications, vol: 41, 1937-1946, 2014.
  • R. Singh, H. Kumar ve R. K. Singla, An intrusion detection system using network traffic profiling and online sequential extreme learning machine, Expert Systems with Applications, vol: 42, 8609-8624, 2015.
  • S. Bhattacharya ve S. Selvakumar, LAWRA: a layered wrapper feature selection approach for network attack detection, Security and Communication Networks, vol: 8, 3459-3468, 2015.
  • M. Mohammadi, B. Raahemi, A. Akbari ve B. Nassersharif, New class-dependent feature transformation for intrusion detection systems, Security and Communication Networks, vol: 5, 1296-1311, 2012.
  • Q. Liu, J. Yin, V. C. M. Leung, J.-H. Zhai, Z. Cai ve J. Lin, Applying a new localized generalization error model to design neural networks trained with extreme learning machine, Neural Computing and Applications, vol: 27, 59-66, 2016.
  • E. D. la Hoz, E. D. L. Hoz, A. Ortiz, J. Ortega ve B. Prieto, PCA filtering and probabilistic SOM for network intrusion detection, Neurocomputing, vol: 164, 71-81, 2015.
There are 42 citations in total.

Details

Journal Section Makaleler
Authors

Atilla Özgür

Hamit Erdem

Publication Date March 8, 2018
Submission Date May 31, 2016
Published in Issue Year 2018 Volume: 33 Issue: 1

Cite

APA Özgür, A., & Erdem, H. (2018). Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 33(1). https://doi.org/10.17341/gazimmfd.406781
AMA Özgür A, Erdem H. Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu. GUMMFD. March 2018;33(1). doi:10.17341/gazimmfd.406781
Chicago Özgür, Atilla, and Hamit Erdem. “Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi Ve Çoklu Sınıflandırıcı Füzyonu”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 33, no. 1 (March 2018). https://doi.org/10.17341/gazimmfd.406781.
EndNote Özgür A, Erdem H (March 1, 2018) Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 33 1
IEEE A. Özgür and H. Erdem, “Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu”, GUMMFD, vol. 33, no. 1, 2018, doi: 10.17341/gazimmfd.406781.
ISNAD Özgür, Atilla - Erdem, Hamit. “Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi Ve Çoklu Sınıflandırıcı Füzyonu”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 33/1 (March 2018). https://doi.org/10.17341/gazimmfd.406781.
JAMA Özgür A, Erdem H. Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu. GUMMFD. 2018;33. doi:10.17341/gazimmfd.406781.
MLA Özgür, Atilla and Hamit Erdem. “Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi Ve Çoklu Sınıflandırıcı Füzyonu”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 33, no. 1, 2018, doi:10.17341/gazimmfd.406781.
Vancouver Özgür A, Erdem H. Saldırı Tespit Sistemlerinde Genetik Algoritma Kullanarak Nitelik Seçimi ve Çoklu Sınıflandırıcı Füzyonu. GUMMFD. 2018;33(1).

Cited By

APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE IN INTRUSION DETECTION SYSTEM CLASSIFICATION USING BOOSTING ALGORITHMS
Mugla Journal of Science and Technology
https://doi.org/10.22531/muglajsci.1343051
Veri setine uygulanan ön işlemler ile makine öğrenimi yöntemi kullanılarak geliştirilen saldırı tespit modellerinin performanslarının arttırılması
Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi
https://doi.org/10.17341/gazimmfd.1122021
A Tree Based Machine Learning and Deep Learning Classification for Network Intrusion Detection
European Journal of Science and Technology
https://doi.org/10.31590/ejosat.889994
Determining Optimal Route for Medication Delivery of Covid-19 Patients with Drone
Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
Yasemin ÇETİN KAYA
https://doi.org/10.29109/gujsc.930903