Endüstriyel Kontrol Sistemlerinde Yenilikçi Anomali Tespit Sistemlerinin İncelenmesi

Yıl 2023, , 34 - 46, 30.04.2023

Kerem Çınar , Murat İskefiyeli

https://doi.org/10.46387/bjesr.1230141

Öz

Endüstriyel Kontrol Sistemleri (ICS) veya SCADA ağları, mimarileri tescilli donanım, yazılım ve protokollerden standart ve açık kaynaklara geçtikçe siber saldırıların hedefi haline gelmektedir. Büyük ölçekli sensör verileri, olağan dışı durumları ve siber saldırı olaylarını sürekli olarak izlenebilir kılmaktadır. Mevcut denetimsiz makine öğrenimi yaklaşımları, anormallikleri tespit etmek için sistemdeki sensörler arasındaki uzamsal-zamansal korelasyonu ve diğer bağımlılıkları tam olarak kullanmamıştır. Bu makale, Konvolüsyonel Sinir Ağı (CNN), Tekrarlayan Sinir Ağı (RNN), Stacked Autoencoder (SAE), Uzun Kısa Süreli Bellek gibi çeşitli mimarilerin SCADA ağlarındaki anomalilerin tespit edilmesinde kullanılan yaklaşımların incelenmesidir. Ayrıca makalede bu yöntemlerin incelenmesine ek olarak Uzun-Kısa Süreli-Bellek Tekrarlayan Sinir Ağlarını (LSTM-RNN) temel modeller (yani, üreteç ve ayrımcı) olarak kullanan, Üretken Çelişkili Ağlara (GAN'lar) dayalı denetimsiz çok değişkenli bir anomalli tespit yöntemini detaylı olarak sunmaktadır.

Anahtar Kelimeler

Derin Öğrenme, GAN, Endüstriyel Kontrol Sistemleri, Makine Öğrenimi

Investigation of Innovative Anomaly Detection Systems in Industrial Control Systems

Öz

Industrial Control Systems (ICS) or SCADA networks are becoming targets of cyber-attacks as their architectures move from proprietary hardware, software, and protocols to standard and open sources. Large-scale sensor data makes anomalies and cyber-attack events continuously monitored. Current unsupervised machine learning approaches have not fully exploited the spatiotemporal correlation and other dependencies between sensors in the system to detect anomalies. This article reviews the approaches used to detect anomalies in SCADA networks of various architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Stacked Autoencoder (SAE), and Long Short-Term Memory. In addition to reviews of these methods in the article, an unsupervised multivariate anomaly detection method based on Generative Contradictory Networks (GANs) using Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) as basic models (i.e. generator and discriminator) is presented.

Anahtar Kelimeler

Deep Learning, Industrial Control Systems, Machine Learning, GAN

Kaynakça

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