Classification of Distributed Denial of Service Attacks Using Machine Learning Methods

Year 2024, Volume: 5 Issue: 1, 15 - 20, 29.06.2024

Uğur İnce , Gülşah Karaduman

https://doi.org/10.46572/naturengs.1450965

Abstract

With the digitalized world, the uninterrupted provision of services over the internet, especially in hospitals, banking, energy, etc. systems is of great importance. There are many attack methods to disrupt or disable these services. Denial of service attacks, which are one of these methods, are more complex and difficult to detect; Organizing such attacks becomes very easy and cost-effective thanks to many tools. Attackers can perform DDoS attacks on target systems with very little knowledge and skills, and they can render target systems inoperable, sometimes for a short time or for days. This work aims to use machine learning techniques to classify Ddos attacks with high accuracy. The CIC-DDoS2019 dataset, which is the most up-to-date and comprehensive attack dataset on the internet, was used. The data obtained after various data preprocessing processes were classified by machine learning methods and the accuracy rates, recall, f1-score and precision values in these methods were compared.

Keywords

Distributed Denial of Service Attacks, Machine Learning, Denial of Service Attacks

References

• https://recrodigital.com/dunyada-ve-turkiyede-internet-sosyal-medya-kullanimi-2022, Access May 2023
• https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2022-45587, Access April 2023
• Hekim, H., BAŞIBÜYÜK, O. (2013). Siber Suçlar ve Türkiye’nin Siber Güvenlik Politikalari. Uluslararası Güvenlik ve Terörizm Dergisi, 4(2), 135-158.
• Atasever, S., Özçelik, İ., Sağıroğlu, Ş. (2019). Siber Terör ve DDoS, Süleyman Demirel Üniversitesi Fen Bilimleri Dergisi, Cilt 23, Sayı 1, 238-244.
• Masum, E., Samet, R. (2018). Mobil Botnet ile Ddos Saldırısı, Bilişim Teknolojileri Dergisi, Cilt:11, Sayı:2, Nisan
• Doshi, R., Apthorpe, N. ve Feamster, N. (2018). Machine learning ddos detection for consumer internet of things devices, 2018 IEEE Symposium on Security and Privacy Workshops, 29-35
• Shanmuga, S., Sivaram, M. ve Jayanthiladevi, A. (2020). Machine learning based DDOS detection, 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), AISSMS Institute of Information Technology, Pune, India. Mar 12-14, 2020, 29-35.
• Özçam, B. (2021). DDoS Atak Tespiti İçin Makine Öğrenmesi Algoritmaları ile Anomaly Tespiti, Yüksek Lisans Tezi, İstanbul Ticaret Üniversitesi, Fen Bilimleri Enstitüsü.
• Manjula, H.T., Mangla, N. (2022). An approach to on-stream DDoS blitz detection using machine Learning Algorithms, Materials Today: Proceedings, 1-8
• Aastha Maheshwari, Burhan Mehraj, Mohd Shaad Khan, Mohd Shaheem Idrisi, An optimized weighted voting based ensemble model for DDoS attack detection and mitigation in SDN environment,Volume 89, 2022,104412, ISSN 0141-9331, https://doi.org/10.1016/j.micpro.2021.104412.
• Akgun, D., Hizal, S., Cavusoglu, U.(2022). A new DDoS attacks intrusion detection model based on deep learning for cybersecurity,Computers & Security,Volume 118,ISSN 0167-4048,2022, https://doi.org/10.1016/j.cose.2022.102748.
• Atasever, S., Özçelik, İ., Sağıroğlu, Ş. (2019). Siber Terör ve DDoS, Süleyman Demirel Üniversitesi Fen Bilimleri Dergisi, Cilt 23, Sayı 1, 238-244.
• https://www.infosecurity-magazine.com/blogs/2022-ddos-yearinreview/, Acces March 2023
• Asarkaya, S., Kaynar, O.,Yelmen, İ., Yıldırım, F.,Zontul, M. (2021). Tasarım Mimarlık ve Mühendislik Dergisi, Cilt 1, Sayı 3, 2021, 221 – 232.
• https://www.barikat.com.tr/images/blog/loddos-ddos-saldirilari-degerlendirme-raporu.pdf, Access May 2023
• Meng, T., Jing, X., Yan, Z., Pedrycz, W. (2020). A survey on machine learning for data fusion, İnformation Fusion, Cilt: 57, Mayıs 2020, 115-129
• Kaynar, O., Arslan, H., Görmez, Y., Işık, Y.E. (2018). Makine Öğrenmesi ve Öznitelik Seçim Yöntemleriyle Saldırı Tespiti, Bilişim Teknolojileri Dergisi, Cilt:11, Sayı:2, Nisan2018, c doi: 10.17671/gazibtd.368583
• Aastha Maheshwari, Burhan Mehraj, Mohd Shaad Khan, Mohd Shaheem Idrisi, An optimized weighted voting based ensemble model for DDoS attack detection and mitigation in SDN environment, Volume 89, 2022, 104412,ISSN 0141-9331, https://doi.org/10.1016/j.micpro.2021.104412.
• Gökay Emel, G., Taşkın, Ç. (2005). Veri Madenciliğinde Karar Ağaçları ve Bir Satış Analiz Uygulaması, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, Cilt:6, Sayı:2, Aralık 2005, 222-239
• Masum, E., Samet, R. (2018). Mobil Botnet ile Ddos Saldırısı, Bilişim Teknolojileri Dergisi, Cilt:11, Sayı:2, Nisan
• Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, and Ali A. Ghorbani, "Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy", IEEE 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019.