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Nesnelerin İnterneti Saldırılarının Hibrit Öğrenme ve Özellik Seçimi Yöntemi Kullanılarak Tespiti

Year 2021, Issue: 29, 19 - 25, 01.12.2021
https://doi.org/10.31590/ejosat.1017433

Abstract

Nesnelerin interneti (IoT), hayatımızın her alanında kullanılan ve her geçen gün internetteki veri sayısını artıran muazzam miktarda veri üretmektedir. Akıllı saatler, robot süpürgeler, kameralı buzdolapları ve daha kullanılan birçok cihaz IoT cihazları olarak kabul edilebilir. Ayrıca gelişen teknoloji ile birlikte hayatımızın her alanında olan internete erişim kolaylığı insanlara avantajlar sağladığı gibi dezavantajlar da sağlamaktadır. Kötü amaçlı yazılımlar ve saldırganlar, yoğun olarak kullandığımız cihazlara ve önemli bilgilerimize internet üzerinden daha kolay erişebilmektedir. Bu noktada özellikle IoT cihazlarında veri gizliliği ve güvenliği büyük önem kazanmaktadır çünkü kullandığımız akıllı saatler veya kullandığımız buzdolapları aracılığıyla kişisel verilerimize erişim bireyler ve aileleri için büyük bir tehdit oluşturabilmektedir. Tüm bu durumlar göz önüne alındığında bu çalışma, veri ön işlemenin önemine ve IoT cihazları için hibrit bir makine öğrenmesi tabanlı saldırı tespit sistemi (IDS) geliştirmeye odaklanmaktadır. Çalışmada yapılacak araştırmalar için popüler bir makine öğrenme algoritması olan Karar Ağacı ve n_Balot veri kümesi tercih edilmiştir. Buna göre veri azaltma işlemi ve özellik seçimi ile n_Balot veri kümesine K-means ve Karar Ağacı algoritmaları uygulanarak saldırı tespiti yapan hibrit bir model oluşturulması amaçlanmıştır. Veri ön işlemede, Ki-Kare seçim yöntemi ile özellik seçimi ve RandomOverSampling yöntemi ile veri azaltma işlemleri yapılmıştır. Daha sonra veri sayısı azaltılmış ve özellik seçimi gerçeklenerek işlenmiş veri kümesine K-Means algoritması uygulanarak kümeleme yapılmış ve kümeleme ile elde edilen sonuçlar Karar ağacı algoritması ile sınıflandırılmıştır. Yapılan tüm incelemeler sonucunda hiçbir işlem yapılmadan yani veri ön işleme ve özellik seçimi gerçekleştirmeden sadece Karar Ağacı ile yapılan tahminlerde hata oranı %0,39 iken, geliştirilen hibrit model ile hata oranı %0,01'e düşürülmüştür.

References

  • Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020). TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access, 8, 165130-165150.
  • Anthi, E., Williams, L., Słowińska, M., Theodorakopoulos, G., & Burnap, P. (2019). A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), 9042-9053.
  • Ashton, K. (2009). That ‘internet of things’ thing. RFID journal, 22(7), 97-114.
  • Bayazit, E. C., Sahingoz, O. K., & Dogan, B. (2020, June). Malware detection in Android systems with traditional machine learning models: a survey. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-8). IEEE.
  • Derhab, A., Aldweesh, A., Emam, A. Z., & Khan, F. A. (2020). Intrusion detection system for Internet of Things based on temporal convolution neural network and efficient feature engineering. Wireless Communications and Mobile Computing, 2020.
  • Foley, J., Moradpoor, N., & Ochenyi, H. (2020). Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset. Security and Communication Networks, 2020.
  • Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics), 28(1), 100-108.
  • Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P. L., Iorkyase, E., Tachtatzis, C., & Atkinson, R. (2016, May). Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
  • Karatas, G., Demir, O., & Sahingoz, O. K. (2020). Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset. IEEE Access, 8, 32150-32162.
  • Korkmaz, M., Sahingoz, O. K., & Diri, B. (2020, June). Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-9). IEEE.
  • Korkmaz, M., Sahingoz, O. K., & Diri, B. (2020, July). Detection of Phishing Websites by Using Machine Learning-Based URL Analysis. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
  • Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern recognition, 36(2), 451-461.
  • Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3), 12-22.
  • Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks, 11(8), 2661-2674.
  • Parra, G. D. L. T., Rad, P., Choo, K. K. R., & Beebe, N. (2020). Detecting Internet of Things attacks using distributed deep learning. Journal of Network and Computer Applications, 163, 102662.
  • Sforzin, A., Mármol, F. G., Conti, M., & Bohli, J. M. (2016, July). Rpids: Raspberry pi ids—a fruitful intrusion detection system for iot. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) (pp. 440-448). IEEE.
  • Smys, S., Basar, A., & Wang, H. (2020). Hybrid intrusion detection system for internet of Things (IoT). Journal of ISMAC, 2(04), 190-199.
  • Tallarida, R. J., & Murray, R. B. (1987). Chi-Square Test. Manual of Pharmacologic Calculations.
  • Yang, K., Ren, J., Zhu, Y., & Zhang, W. (2018). Active learning for wireless IoT intrusion detection. IEEE Wireless Communications, 25(6), 19-25.
  • Yen, S. J., & Lee, Y. S. (2009). Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications, 36(3), 5718-5727.

Detecting Internet of Things Attacks by Using Hybrid Learning and Feature Selection Method

Year 2021, Issue: 29, 19 - 25, 01.12.2021
https://doi.org/10.31590/ejosat.1017433

Abstract

Internet of Things (IoT) produces an enormous amount of data, which is used in all areas of our lives and increases the number of data on the Internet with each passing day. Smart watches, robot vacuum cleaners, refrigerators with cameras, and more can all be considered IoT devices. Ease of access to the Internet provides people with advantages as well as disadvantages. Malware and intruders have easier access to the devices we use and our information via the internet. At this point, data security gains great importance especially in IoT devices because accessing our personal data via smart watches or refrigerators we use can pose a great threat to individuals and their families. This study focus the importance of data preprocessing and developing a hybrid machine learning-based intrusion detection system (IDS) for IoT. Decision Tree, which is a popular machine learning algorithm, and n_Balot dataset were preferred for investigations. Accordingly, it is aimed to create a hybrid model by applying K-means and Decision Tree algorithms to the n_Balot dataset with under sampling and feature selection. In the data preprocessing, feature selection was performed with Chi-Square method and under sampling performed with RandomOverSampling method. Then, clustering was done by applying K-means to the processed dataset, and the results obtained with the clustering were classified with the Decision tree algorithm. As a result of the study, while the error rate was 0.39% in the predictions made only with the decision tree, the error rate was reduced to 0.01% with the developed hybrid model.

References

  • Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020). TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access, 8, 165130-165150.
  • Anthi, E., Williams, L., Słowińska, M., Theodorakopoulos, G., & Burnap, P. (2019). A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), 9042-9053.
  • Ashton, K. (2009). That ‘internet of things’ thing. RFID journal, 22(7), 97-114.
  • Bayazit, E. C., Sahingoz, O. K., & Dogan, B. (2020, June). Malware detection in Android systems with traditional machine learning models: a survey. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-8). IEEE.
  • Derhab, A., Aldweesh, A., Emam, A. Z., & Khan, F. A. (2020). Intrusion detection system for Internet of Things based on temporal convolution neural network and efficient feature engineering. Wireless Communications and Mobile Computing, 2020.
  • Foley, J., Moradpoor, N., & Ochenyi, H. (2020). Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset. Security and Communication Networks, 2020.
  • Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics), 28(1), 100-108.
  • Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P. L., Iorkyase, E., Tachtatzis, C., & Atkinson, R. (2016, May). Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
  • Karatas, G., Demir, O., & Sahingoz, O. K. (2020). Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset. IEEE Access, 8, 32150-32162.
  • Korkmaz, M., Sahingoz, O. K., & Diri, B. (2020, June). Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-9). IEEE.
  • Korkmaz, M., Sahingoz, O. K., & Diri, B. (2020, July). Detection of Phishing Websites by Using Machine Learning-Based URL Analysis. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
  • Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern recognition, 36(2), 451-461.
  • Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3), 12-22.
  • Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks, 11(8), 2661-2674.
  • Parra, G. D. L. T., Rad, P., Choo, K. K. R., & Beebe, N. (2020). Detecting Internet of Things attacks using distributed deep learning. Journal of Network and Computer Applications, 163, 102662.
  • Sforzin, A., Mármol, F. G., Conti, M., & Bohli, J. M. (2016, July). Rpids: Raspberry pi ids—a fruitful intrusion detection system for iot. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) (pp. 440-448). IEEE.
  • Smys, S., Basar, A., & Wang, H. (2020). Hybrid intrusion detection system for internet of Things (IoT). Journal of ISMAC, 2(04), 190-199.
  • Tallarida, R. J., & Murray, R. B. (1987). Chi-Square Test. Manual of Pharmacologic Calculations.
  • Yang, K., Ren, J., Zhu, Y., & Zhang, W. (2018). Active learning for wireless IoT intrusion detection. IEEE Wireless Communications, 25(6), 19-25.
  • Yen, S. J., & Lee, Y. S. (2009). Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications, 36(3), 5718-5727.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Gözde Karataş Baydoğmuş 0000-0003-2303-9410

Early Pub Date December 15, 2021
Publication Date December 1, 2021
Published in Issue Year 2021 Issue: 29

Cite

APA Karataş Baydoğmuş, G. (2021). Detecting Internet of Things Attacks by Using Hybrid Learning and Feature Selection Method. Avrupa Bilim Ve Teknoloji Dergisi(29), 19-25. https://doi.org/10.31590/ejosat.1017433