SDN Tabanlı SCADA Sistemlerinde Makine Öğrenmesi Tabanlı DDoS Saldırı Tespiti

Abstract

Kritik altyapılardaki süreçleri izlemek ve denetlemek için Denetleyici Kontrol ve Veri Toplama (SCADA) sistemleri kullanılmaktadır. SCADA sistemleri gelişen siber saldırılar karşısında yeterli tespit ve savunma mekanizmalarına sahip değildir ve birçok güvenlik açıklığı barındırmaktadır. Ulusal ve uluslararası öneme sahip kritik altyapılarda SCADA sistemlerinin kullanılması kötü niyetli saldırganlar için yeni hedefler anlamına gelmektedir. Ayrıca SCADA sistemlerinin yeni teknolojilerle birlikte kullanılması güvenlik dünyasına yeni bakış açıları kazandırmaktadır. SDN gibi teknolojiler SCADA sistemleriyle bütünleştirildiğinde, sisteme yönetilebilirlik ve programlanabilirlik konularında avantajlar kazandırmaktadır. Bunun yanı sıra DDoS gibi saldırılara karşı güvenlik sorunları da barındırmaktadır. Bu sebeplerden dolayı SCADA sistemlerinin siber güvenliğinin sağlanması zorunlu hale gelmiştir. Bu çalışmada SDN tabanlı SCADA sistemlerinin DDoS saldırılarına maruz kalması durumu ele alınmıştır. Saldırı tespitinin yapılması için Logistic Regression, K-Nearest Neighbors, Random Forest ve Support Vector Machine sınıflandırma algoritmaları kullanılmıştır. Hazır bir veriseti üzerinde çalışılmış ve buna göre en doğru tespiti gerçekleştiren model çalışmamızda önerilmiştir. Sonuçlar önerilen SVM sınıflandırıcı modelinin (%97.2 oranında doğruluk), SDN tabanlı SCADA sistemlerine yönelik DDoS saldırılarını etkili bir şekilde tespit ettiğini göstermiştir.

Keywords

• SCADA
• SDN
• DDoS
• Makine Öğrenmesi
• Modbus Protokolü

Machine Learning-Based DDoS Attack Detection on SDN-Based SCADA Systems

Abstract

Supervisory Control and Data Acquisition (SCADA) systems are used to monitor and control processes in critical infrastructures. SCADA systems do not have adequate detection and defense mechanisms against developing cyber attacks and contains many security vulnerabilities. The use of SCADA systems in critical infrastructures of national and international importance means new targets for malicious attackers. In addition, the use of SCADA systems with new technologies brings new perspectives to the security world. When technologies such as SDN are integrated with SCADA systems, it brings advantages to the system in terms of manageability and programmability. However security problems also occur against attacks such as DDoS. For these reasons, it is imperative to ensure the cyber security of SCADA systems. In this study, the case of SDN-based SCADA systems exposed to DDoS attacks is discussed. Logistic Regression, K-Nearest Neighbors, Random Forest, and Support Vector Machine classification algorithms have been used for attack detection. A ready-made dataset has been studied, and accordingly, the model that makes the most accurate determination has been proposed in our study. The results show that the proposed SVM classifier model (97.2% accuracy rate) effectively detects DDoS attacks against SDN-based SCADA systems.

Keywords

• SCADA
• SDN
• DDoS
• Machine Learning
• Modbus Protocol

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