MAKİNE VE DERİN ÖĞRENME YÖNTEMLERİ İLE NESNELERİN İNTERNETİ İÇİN SALDIRI TESPİTİNİN KARŞILAŞTIRILMASI

Year 2022, Volume: 18 Issue: 2, 333 - 361, 30.11.2022

Siham Amarouche , Kerem Küçük

Abstract

Günümüz teknoloji dünyasında, nesnelerin interneti (IoT) sistemleri için izinsiz giriş tespiti önemli bir konudur. IoT'de kablosuz ağlara bağlı küçük cihazların kullanımının artmasıyla birlikte veri miktarı da hızla artıyor. Bu veriler saldırılara karşı savunmasız olabilir, bu nedenle IoT sistemlerinin sistemin gizliliğini, kullanılabilirliğini ve güvenilirliğini artırmak için bu verileri güvenceye alması gerekir. Yapay zekayı (AI) otonom olarak kullanarak saldırıları tespit etme ilerlemesi, ağ saldırı tespit sistemlerinde (NIDS) daha uygun bir yöntem haline geldi. Bu yazıda, NIDS'de performansı iyileştirmek ve doğruluğu artırmak için yeni tespit tekniği öneriyoruz. IoT sistemleri için farklı saldırı türlerini tespit etmek için farklı makine öğrenimi (ML) ve derin öğrenme (DL) yöntemleri sunuyoruz. Ayrıca, IoT sistem ortamındaki anomaliyi tanımlamanın en iyi yolunu bulmak için deneyler sunuyoruz, farklı AI modelleri arasında karşılaştırmalar yapıyoruz. Deney, açık veri tabanı UNSW-NB15 ile değerlendirilmiştir.

Keywords

Derin Öğrenme, IoT Güvenliği, Makine Öğrenmesi, Saldırı Tespiti, Siber Güvenlik.

MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS

Abstract

In today’s technology world, intrusion detection is important topic for the Internet of Things (IoT) systems. With the growth of using tiny devices connected to wireless networks in IoT, the amount of data is growing rapidly. This data may be vulnerable to attacks so that IoT systems need to secure it for increasing the system’s confidentiality, availability, and reliability. The progress of detecting attacks using artificial intelligence (AI) autonomously has become a more convenient method in network intrusion detection systems (NIDS). In this article, we propose new detecting technique to improve performance and increase accuracy in NIDS. We present different machine learning (ML) and deep learning (DL) methods to detect the different type of attacks for IoT systems. We also provide the experiments to find out the best way to identify the anomaly in IoT system environment, take comparisons between different AI models. The experiment was evaluated with the open database UNSW-NB15.

Keywords

Deep Learning, IoT Security, Machine Learning, Intrusion Detection, Cyber Security.

References

• Referans1 Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), 1-41. doi:10.1016/j.heliyon.2018.e00938
• Referans2 Albawi, S., Mohammed, T.A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Turkey, IEEE. doi:10.1109/ICEngTechnol.2017.8308186
• Referans3 Alsamiri, J., & Alsubhi, K. (2019). Internet of Things cyber attacks detection using machine learning. International Journal of Advanced Computer Science and Applications, 10(12), 627-634.‏ doi:10.14569/ijacsa.2019.0101280
• Referans4 Amidi, S., & Amidi, A. (2019). Recurrent Neural Networks Cheatsheet. Stanford Education. Retrieved August 19, 2021, from https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks#overview
• Referans5 Boateng, E. Y., & Abaye, D. A. (2019). A review of the logistic regression model with emphasis on medical research. Journal of Data Analysis and Information Processing, 7(4), 190-207.‏ doi:10.4236/jdaip.2019.74012
• Referans6 Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
• Referans7 Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.‏ doi:10.1016/j.compeleceng.2013.11.024
• Referans8 Chomboon, K., Chujai, P., Teerarassamee, P., Kerdprasop, K., & Kerdprasop, N. (2015). An empirical study of distance metrics for k-nearest neighbor algorithm. In Proceedings of the 3rd international conference on industrial application engineering (pp. 280-285).‏ Japan, The Institute of Industrial Applications Engineers. doi:10.12792/iciae2015.051
• Referans9 Gated recurrent unit. (2021, December 29). In Wikipedia. Retrieved June 23, 2021, from https://en.wikipedia.org/wiki/Gated_recurrent_unit
• Referans10 Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.‏ doi:10.1162/neco.1997.9.8.1735
• Referans11 Jonathan, J. (n.d.). What’s a Deep Neural Network? Deep Nets Explained. BMC Software Blogs. Retrieved March 10, 2021, from https://www.bmc.com/blogs/deep-neural-network/
• Referans12 Jozefowicz, R., Zaremba, W., & Sutskever, I. (2015, June). An empirical exploration of recurrent network architectures. In F. R. Bach & D. M. Blei (eds.), International conference on machine learning (pp. 2342-2350). PMLR.‏
• Referans13 Kasongo, S. M., & Sun, Y. (2020). Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. Journal of Big Data, 7(1), 1-20.‏ doi:10.1186/s40537-020-00379-6.
• Referans14 Kaviani, P., & Dhotre, S. (2017). Short survey on naive bayes algorithm. International Journal of Advance Engineering and Research Development, 4(11), 607-611.‏
• Referans15 Khraisat, A., Gondal, I., & Vamplew, P. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2, 1-20.‏ doi:10.1186/s42400-019-0038-7
• Referans16 Long short-term memory. (2021, June 21). In Wikipedia. Retrieved June 23, 2021, from https://en.wikipedia.org/wiki/Long_short-term_memory#cite_note-lstm1997-1
• Referans17 Manaswi, N. K. (2018). Deep learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition with Tensorflow and Keras. Apress, Berkeley, CA.
• Referans18 Moustafa, N., & Slay, J. (2015, November). UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 military communications and information systems conference (MilCIS) (pp. 1-6). Australia, IEEE.‏ doi: 10.1109/MilCIS.2015.7348942
• Referans19 Patel, K. K., & Patel, S. M. (2016). Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges. International Journal of Engineering Science and Computing, 6(5), 6122-6131. doi:10.4010/2016.1482
• Referans20 Recurrent neural network. (2021, June 8). In Wikipedia. Retrieved June 23, 2021, from https://en.wikipedia.org/wiki/Recurrent_neural_network Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660-674. doi:10.1109/21.97458
• Referans21 Thakur, D. (2018). LSTM and its Equations. Medium. Retrieved August 19, 2021, from https://medium.com/@divyanshu132/lstm-and-its-equations-5ee9246d04af
• Referans22 Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017). Evaluation of recurrent neural network and its variants for intrusion detection system (IDS). International Journal of Information System Modeling and Design (IJISMD), 8(3), 43-63.‏ doi:10.4018/IJISMD.2017070103