Optimize Edilmiş Makine Öğrenimi Algoritmaları Kullanarak Internet Ağı Saldırı Tespiti

Year 2021, Issue: 25, 463 - 474, 31.08.2021

Nurdan Akhan Baykan , Tahira Khorram

https://doi.org/10.31590/ejosat.849723

Cited By: 3

Abstract

Internet ağı saldırı tespit mekanizması, mevcutta hızlı büyüyen ağ sistemlerinde birincil gereksinimdir. Veri madenciliği ve makine öğrenimi yaklaşımları, son birkaç yıldır ağ anomali tespiti için yaygın olarak kullanılmaktadır. Makine öğrenimi tabanlı saldırı tespit sistemleri son zamanlarda daha popüler hale gelmektedir. Saldırı Tespit Sistemi (STS) için en yaygın olarak kullanılan makine öğrenimi algoritmaları K-En Yakın Komşu (KNN), Destek Vektör Makinesi (DVM) ve Rastgele Orman (RO) algoritmalarıdır. Ancak bu yöntemlerin performansı, uygun parametre değerlerinin seçimine bağlıdır. Bu araştırma, etkili makine öğrenme algoritmalarına dayalı bir STS modeli oluşturma amacına odaklanmaktadır. Bu araştırmada kullanılan makine öğrenme algoritmaları KNN, DVM ve RO’dır. Bu algoritmaların sınıflandırma doğruluğunu iyileştirmek için algoritmaların bazı parametreleri Parçacık Sürü Optimizasyonu (PSO) ve Yapay Arı Kolonisi (YAK) optimizasyon teknikleri kullanılarak optimize edilmiştir. Çalışmanın sonucu, parametreleri optimize edilmiş KNN, DVM ve RO’nın, orijinal parametre değerleri ile kullanımlarından daha iyi performans gösterdiğini göstermektedir. Ayrıca, deney sonuçları, hem bilinen ağ saldırılarının hem de bilinmeyen ağ saldırılarının tespiti ile ilgili olarak ağ anomali tespitinde KNN’nin en uygun algoritma olduğunu göstermektedir. Bu araştırma kapsamında çalışmalarda NSL-KDD standart veri seti kullanılmıştır. Çalışmada önerilen modelin, son teknoloji modellerde sağlanandan daha iyi performans gösterdiği kanıtlanmıştır.

Keywords

Anomali Tespiti, Akıllı Saldırı Tespit Sistemi, Sürü Zekâsı, Makine Öğrenimi Algoritmaları

Network Intrusion Detection using Optimized Machine Learning Algorithms

Abstract

Network intrusion detection mechanism is a primary requirement in the current fast-growing network systems. Data mining and machine learning approaches are widely used for network anomaly detection during past few years. Machine learning based intrusive activity detector is becoming more popular. The most commonly used machine learning algorithms for Intrusion Detection System (IDS) are K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). However, the performance of these methods is reliant upon the selection of the proper parameter values. This research focuses its aim to build an IDS model based on the most effective algorithms. The machine learning algorithms are used in this research are KNN, SVM and RF. To improve these algorithms classification accuracy, some parameters of the algorithms are optimized using Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization techniques, while other parameters are used with default values. The result of this experiment shows that optimized KNN, SVM and RF perform better than these algorithms with their default parameter values. Furthermore, the results the experiment shows that KNN is the most suitable algorithm for network anomaly detection regarding detection of known network attacks and unknown network attacks. NSL-KDD standard dataset is used for the experiments of this research. It has been proven that our proposed model performs better than what is provided in the state-of-arts models.

Keywords

Anomaly Detection, Intelligent Intrusion Detection System, Swarm Intelligence, Machine Learning Algorithms

References

• Ganapathy, S., Kulothungan, K., Muthurajkumar, S., Vijayalakshmi, M., Yogesh, P., & Kannan, A. (2013). Intelligent feature selection and classification techniques for intrusion detection in networks. A survey. EURASIP Journal on Wireless Communications and Networking, 913-921.
• Mukherjee, S. and Sharma, N., (2012). Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technology, 119-128.
• Med, A., Lisitsa, A., & Dixon, C., (2011). A misuse-based network intrusion detection system using temporal logic and stream processing. IEEE Network and System Security (NSS), 5th International Conference on, Milan.
• Butun, I., Morgera., S., D., & Sankar., R., (2013). A Survey of Intrusion Detection Systems in Wireless Sensor Networks, IEEE Communications Surveys and Tutorials, 266-182.
• Karaboga, D., (2005). An idea on honey bee swarm for numerical optimization. Kayseri: Erciyes University,
• Dhanabal, L., & Shantharajah, S. (2015). A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 446-451.
• Volden, H., H. (2016). Anomaly detection using Machine learning techniques. Oslo: University of Oslo.
• Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE communication surveys and tutorials,1153-1175.
• Farnaaz, N., & Jabbar, M. (2016). Random Forest Modeling for Network Intrusion Detection System, Procedia Computer Science, 213-217.
• Aburomman, A., & Bin Ibne Reaz, A. M. (2016). A novel SVM-kNN-PSO ensemble method for intrusion detection system. Applied Soft Computing, 2016, pp. 360-372.
• Enache, A.-C., & Patriciu, V. V. (2014). Intrusions Detection Based On Support Vector Machine Optimized with Swarm Intelligence. 9th IEEE International Symposium on Applied Computational Intelligence and Informatics, Romania: IEEE.
• Liao, Y., & Vemuri, V. R. (2002). Use of K-Nearest Neighbor classifier for intrusion detection. Computers and Security, 439-448.
• Roughgarden,T., Algorithms, Retrieved from Coursera: http://class.coursera.org/algo-004/lecture/preview, July 30, 2017
• Eberhart, R.C., & Kennedy, J. (1999). A new optimizer using particle swarm theory. In Proceedings of the 6th international symposium on micro machine and human science (pp. 39-43). Nagoya, Japan.
• Yan, X., (2011). Metaheuristic Optimization Algorithms. http://www.scholarpedia.org/article/Metaheuristic_Optimization.
• Çiftçioğlu, A.Ö., Doğan, E. (2019). Çelik Çerçevelerin Stokastik Yöntemler Kullanılarak Optimum Boyutlandırılması, Konya Mühendislik Bilimleri Dergisi 7(4), 847-861.
• Karakoyun, M., Baykan, N.A., Hacibeyoglu, M. (2017). Multi-Level Thresholding for Image Segmentation with Swarm Optimization Algorithms, International Research Journal of Electronics & Computer Engineering, Vol:30.
• Celtek, S.A., Durdu, A. (2020). An Operant Conditioning Approach For Large Scale Social Optimization Algorithms. Konya Mühendislik Bilimleri Dergisi 8(SI), 38-45.
• Beşkirli, M., Tefek, M.F. (2019). Parçacık Sürü Optimizasyon Algoritması Kullanılarak Optimum Robot Yolu Planlama, Avrupa Bilim Teknoloji Dergisi SI, 201-213.
• Tefek, M.F., Beşkirli, M. (2019). Tesis Yerleştirme (p-Hub) Probleminin Yapay Arı Kolonisi Kullanılarak Çözülmesi. Avrupa Bilim Teknoloji Dergisi SI, 193-200.
• Bansal, J., C., Sharma, H., Jadon, S., S. (2013). Aritificial Bee colony Algorithm: A Survey. Internation Journal of advanced Intelligence Paradigms.
• Lakhina, S., Joseph, S., Verma, B. (2010). Feature reduction using PCA for effect anomaly intrusion detection on NSL-KDD. International Journal of Science Engineering and Technology.
• Yan, X. (2010). Metaheuristic Optimization, Nature-Inspired Algorithms and Applications. Cambridge University Press.
• Roy, S., S., Mittal, D., and Biba, M., (2016). Random Forest Support Vector Machine and Nearest Centriod Method for Classifying Network Intrusion. Computer Science Series, 9–17.
• Wang, J., Hong, X., and Ren, R., R., (2009). A Real-time Intrusion Detection System Based on PSO-SVM. Proceedings of the 2009 International Workshop on Information Security and Applications, Qingdao-china.
• Wang, J., Li, T., and Ron, R., R. (2010). A Real-time IDS Based on Artificial Bee Colony-Support Vector Machine Algorithm. IEEE Third International Workshop in Advanced Computational Intelligence.
• Khorram, T., Network Anomaly Detection Using Optimized Macine Learning Algorithms, Master Thesis, Graduate School of Natural Sciences, Selcuk University, Turkey.