INTERNET OF THINGS BOTNET DETECTION VIA ENSEMBLE DEEP NEURAL NETWORKS

Yıl 2023, Cilt: 7 Sayı: 2, 191 - 197, 31.08.2023

Yağız Onur Kolcu , Ahmet Haşim Yurttakal , Berker Baydan

https://doi.org/10.46519/ij3dptdi.1293277

Öz

The widespread use of the Internet of Things (IoT) and the rapid increase in the number of devices connected to the network bring both benefits and many problems. The most important of these problems is cyber attacks. These cyber attacks cause financial losses as well as loss of reputation and time. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) are used to eliminate or minimize these losses. IDS are designed to be signature-based or anomaly-based, and are currently being developed using anomaly-based systems as machine learning methods. The aim of this study is to detect whether there is an attack on your network, with a high success rate, by considering botnet as one of the attack types. In order to develop this system, it is aimed to use Ensemble Deep Neural Networks (DNN), which is one of the machine learning methods, and to search for solution methods for the most accurate result. In the study, N-BaIoT dataset in the UCI Machine Learning library was used for scientific research. The data consists of 1 benign network stream and 9 malicious network streams carried by 2 botnets. Stacked ensemble of DNN networks has been used from the classification stage. The proposed method has achieved %99 accuracy and the results are encouraging for future studies.

Anahtar Kelimeler

Botnet, Internet of Things, Ensemble, Deep Neural Network, Cyber Threats

Kaynakça

• 1. Elkhodr, M., Shahrestani S. and Cheung, H. "The Internet of Things: Vision & Challenges", IEEE 2013 Tencon-Spring, Pages 218-222, Sydney, 2013.
• 2. Barrera, D., Molloy, I. and Huang, H. "IDIoT: Securing the Internet of Things like it's 1994," arXiv preprint arXiv:1712.03623, 2017.
• 3. Huyghue, B.D. "Cybersecurity, Internet of Things, and Risk Management for Businesses", Diss. Utica College, Utica, NY, 2021.
• 4. Skorin-Kapov, N. et al. "Physical-Layer Security in Evolving Optical Networks." IEEE Communications Magazine, Vol. 54, Issue 8, Pages 110-117, 2016.
• 5. Gantz J. and David, R. "The digital universe in 2020: Big Data, Bigger Digital Shadows and Biggest Growth in the Far East." IDC iView: IDC Analyze the future 2007, Pages 1-16, 2012
• 6. Ahmetoğlu, H. and Daş, R., "Derin Öğrenme ile Büyük Veri Kumelerinden Saldırı Türlerinin Sınıflandırılması", IDAP, Pages 455-463, Malatya, Türkiye, 2019.
• 7. Bezerra, V.H. et al, "IoTDS: A One-Class Classification Approach To Detect Botnets in Internet of Things Devices." Sensors, Vol. 19, Issue 14, 2019.
• 8. Bertino E. and Islam, N. "Botnets and Internet of Things Security." Computer, Vol. 50, Issue 2, Pages 76-79, February 2017.
• 9. Grizzard J.B. et al, "Peer-to-Peer Botnets: Overview and Case Study," HotBots, Vol. 7, Pages 1-8, 2007,
• 10. Algelal, Z. et al, "Botnet Detection Using Ensemble Classifiers of Network Flow", International Journal of Electrical and Computer Engineering (IJECE), Vol. 10, Issue 3, Pages 25-43, 2020.
• 11. Geer, D., "Malicious Bots Threaten Network Security." Computer, Vol. 38, Issue 1, Pages 18-20, January 2005.
• 12. El Naqa, I. and Murphy, M. J. "What is Machine Learning?" Machine Learning in Radiation Oncology, Pages 3-11, Springer, Cham, 2015.
• 13. Yurttakal, A. H., & Erbay, H. “Segmentation of Larynx histopathology images via convolutional neural networks” In Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23, Pages 949-954. Springer International Publishing, 2021.
• 14. Çinarer, G., Emiroğlu, B. G., & Yurttakal, A. H. “Predicting 1p/19q chromosomal deletion of brain tumors using machine learning” Emerging Materials Research, Vol. 10, Issue 2, Pages 238-244, 2021
• 15. Yurttakal, A. H. “Extreme gradient boosting regression model for soil thermal conductivity” Thermal Science, Vol. 25, Issue 1, Pages 1-7, 2021
• 16. Arslan, R. S., & Yurttakal, A. H. “K-nearest neighbour classifier usage for permission based malware detection in android”. Icontech Internatıonal Journal, Vol. 4, Issue 2, Pages 15-27, 2020. 17. Horasan, F., & Yurttakal, A. H. Darknet Web Traffic Classification via Gradient Boosting Algorithm. International Journal of Engineering Research and Development, Vol. 14, Issue 2, Pages 794-798, 2022.
• 18. Lu, Y., "Artificial Intelligence: A Survey on Evolution, Models, Applications and Future Trends", Journal of Management Analytics, Vol. 6, Issue 1, Pages 1-29, 2019.
• 19. Stevanovic M. and Pedersen, J.M. "On the Use of Machine Learning for Identifying Botnet Network Traffic." Journal of Cyber Security and Mobility, Vol. 4, Issue 2 & 3,Pages 109-128, 2016.
• 20. Verma, A. and Ranga, V., "Machine learning Based Intrusion Detection Systems for IoT Applications", Wireless Personal Communications, Vol. 111, Issue 4, Pages 2287-2310, 2020.
• 21. Altunay, H. C. and Albayrak, Z., "Network Intrusion Detection Approach Based on Convolutional Neural Network." Avrupa Bilim ve Teknoloji Dergisi, Vol. 26, Pages 22-29, 2021.
• 22. Meidan, Y. et al. "N-Baiot—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders." IEEE Pervasive Computing, Vol. 17, Issue 3, Pages 12-22, 2018.
• 23.Mirsky, Y. et al, "Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection." arXiv preprint arXiv:1802.09089, 2018.
• 24. Rey, V., Sánchez, P. M. S., Celdrán, A. H., & Bovet, G. “Federated learning for malware detection in iot devices”. Computer Networks, Vol. 204, 2022.
• 25. Antonakakis, M., et al. "Understanding The Mirai Botnet." 26th USENIX Security Symposium, Pages 1093-1110, Berkeley, CA, USA, 2017.
• 26. Ryu, S. and Yang, B., "A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection," Journal of Computer and Communications, Vol. 6, Pages 119-129, 2018.
• 27. Liu, H., & Lang, B. “Machine learning and deep learning methods for intrusion detection systems: A survey”. Applied Sciences, Vol. 9, Issue 20, 2019.
• 28.Rezai, A. "Using Ensemble Learning Technique for Detecting Botnet on IoT," SN Computer Science, Vol. 2, Issue 2, Pages 148, 2021.
• 29. Goodfellow, I. Yoshua B. and Aaron, C. “Deep learning”, MIT Press, Cambridge, MA, 2016.
• 30. Rokach, L. “Ensemble-based classifiers.” Artificial Intelligence Review, Vol. 33, Issue 1, Pages 1-39, 2010.
• 31. Wolpert, D. H. "Stacked Generalization." Neural Networks, Vol. 5, Issue 2, Pages 241-259, 1992.
• 32. Yurttakal A.H. and Baş, H. "Possibility Prediction of Diabetes Mellitus at Early Stage Via Stacked Ensemble Deep Neural Network." Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Vol. 21, Issue 4, Pages 812-819, 2021.
• 33. Wai, F.K. et al, "Automated Botnet Traffic Detection Via Machine Learning.", TENCON 2018-2018 IEEE Region 10 Conference, Pages 38-43, Jeju Island, Korea, 2018.
• 34. Srinivasan S. and Kumar, D. "Enhancing the Security in Cyber-World by Detecting the Botnets Using Ensemble Classification Based Machine Learning", Measurement: Sensors, Vol. 25, Pages 2023.
• 35.Bijalwan, A. et al, “Botnet Analysis Using Ensemble Classifier” Perspectives in Science, Vol. 8, Pages 502-504, 2016.
• 36. Velasco-Mata, J., González-Castro, V., Fidalgo, E. et al., “Real-time botnet detection on large network bandwidths using machine learning” Sci Rep, Vol. 13, Pages 4282, 2023.
• 37. Elsayed N. et al., “IoT botnet detection using an economic deep learning model” AIIoT, 2023.