Stealthy Deception Attacks Against SCADA Systems

SCADA protocols for Industrial Control Systems (ICS) are vulnerable to network attacks such as session hijacking. Hence, research focuses on network anomaly detection based on meta--data (message sizes, timing, command sequence), or on the state values of the physical process. In this work we present a class of semantic network-based attacks against SCADA systems that are undetectable by the above mentioned anomaly detection. After hijacking the communication channels between the Human Machine Interface (HMI) and Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a fake view of the industrial process, deceiving the human operator into taking manual actions. Our most advanced attack also manipulates the messages generated by the operator's actions, reversing their semantic meaning while causing the HMI to present a view that is consistent with the attempted human actions. The attacks are totaly stealthy because the message sizes and timing, the command sequences, and the data values of the ICS's state all remain legitimate. We implemented and tested several attack scenarios in the test lab of our local electric company, against a real HMI and real PLCs, separated by a commercial-grade firewall. We developed a real-time security assessment tool, that can simultaneously manipulate the communication to multiple PLCs and cause the HMI to display a coherent system--wide fake view. Our tool is configured with message-manipulating rules written in an ICS Attack Markup Language (IAML) we designed, which may be of independent interest. Our semantic attacks all successfully fooled the operator and brought the system to states of blackout and possible equipment damage

Temporal Phase Shifts in SCADA Networks

In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Previous work showed that in many cases, it is possible to create an automata-based model of the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server, and to use the model to detect anomalies in the traffic. When testing the validity of previous models, we noticed that overall, the models have difficulty in dealing with communication patterns that change over time. In this paper we show that in many cases the traffic exhibits phases in time, where each phase has a unique pattern, and the transition between the different phases is rather sharp. We suggest a method to automatically detect traffic phase shifts, and a new anomaly detection model that incorporates multiple phases of the traffic. Furthermore we present a new sampling mechanism for training set assembly, which enables the model to learn all phases during the training stage with lower complexity. The model presented has similar accuracy and much less permissiveness compared to the previous general DFA model. Moreover, the model can provide the operator with information about the state of the controlled process at any given time, as seen in the traffic phases.Comment: Full version of CPS-SPC'18 short pape

Ethercat tabanlı bir scada sisteminde kural ve makine öğrenmesine dayalı saldırı ve anomali tespiti

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Endüstriyel kontrol sistemleri (EKS) bulundukları konum ve bileşenleri bakımından kritik altyapıya sahip sistemler olup, bilişim teknolojilerinden (BT) bağımsız olarak uygulama alanına göre kendilerine ait kabul ve işleyişleri bulunmaktadır. Bu sistemler, günümüzde otomasyon hiyerarşisinde yer alan seviyeler arası yatay ve dikey entegrasyonun tek bir protokolle sağlanması fikrinden yola çıkılarak Ethernet ile de adapte edilmiş durumdadır. Dolayısıyla EKS'ler hem doğalarından hem de Ethernet üzerinden bilişim teknolojilerinin sunduğu hizmetlerin içerisine dahil edildiklerinden dolayı siber saldırılara karşı tehdit altındadır. Bu durum, çoğunlukla iletişim altyapısı üzerinden gelen saldırıların tespiti için özelinde EKS çözümlerini gerektirir. Bu çalışmada, otomasyon uygulamalarında yaygın bir kullanıma sahip olan, Ethernet tabanlı gerçek zamanlı EtherCAT protokolü için Snort saldırı tespit sistemi üzerinde bilinen ve bilinmeyen saldırıları tespit eden bütüncül bir yapı ve makine öğrenmesi teknikleriyle anomali tespiti olmak üzere ikisi kural biri anomali tespitine dayanan 3 farklı yaklaşım sunulmaktadır. Sistem, geliştirilen önişlemci yardımıyla, bilinen saldırılar için güvenli düğüm yaklaşımı, bilinmeyen saldırılar için ise saha veri yolu tekrar periyodunu tespit ederek istatistiksel tekniklerle ve özgün çözümlerle kural tabanlı olarak saldırı tespitini kapsamaktadır. Tespitler bir günlükleme ve izleme yapısı olan ELK yığını üzerinde kullanıcıya sunulmaktadır. Ayrıca, yine bilinmeyen saldırılar için oluşturulan su seviye kontrol otomasyonu test ortamı üzerinde olaylar gerçeklenerek bir veri seti hazırlanması ve çeşitli öğrenme tekniklerinin veri seti üzerinde anomali tespitini kapsamaktadır. Bilinmeyen saldırıların tespiti kapsamında uygulanan periyot tespitinin %95-%99 doğrulukla yapılabildiği görülmüştür. Önerilen sistem üzerinde ise MAC aldatma, veri enjeksiyonu, DoS, köle saldırıları gibi ataklar gerçeklenmiş, alarm ve günlüklemeler incelendiğinde saldırıların başarıyla tespit edildiği görülmüştür. Ayrıca, k-NN ve SVM GA tekniklerinin olay tespitinde başarılı sonuç verdikleri belirlenmiştir.Industrial control systems (ICS) are critical infrastructures in terms of their location and components. These systems have their own features and operation related to the application field independent from the information technologies (IT). They are also adapted with the Ethernet technologies based on the idea of providing horizontal and vertical integration between the levels in the automation hierarchy with a single protocol. Therefore, ICSs are threatened by cyber attacks, due to both their nature and support of IT services through Ethernet. This risk requires ICS specific solutions to detect and prevent attacks which use communication infrastructure. In this study, two rule based which detect known and unknown attacks on the Snort system and one anomaly based which uses machine learning techniques, in total of three different approaches were presented as a holistic structure for Ethernet based real-time EtherCAT protocol, which is widely used in automation applications. In the case of rule based intrusion detection, the EtherCAT preprocessor was proposed, which applies the trust node approach for known attacks, and identifies the field bus repetition period for unknown attacks, with statistical techniques and novel solutions. The findings were presented to the user on the ELK stack, which is a logging and monitoring structure. For anomaly based intrusion detection, the water level control automation testbed was developed, a dataset was prepared by generating events and various machine learning techniques were applied on the dataset. According to the findings obtained in this research, it was concluded that the period determination which was applied within the scope of unknown attack detection can be made with 95% - 99% accuracy. When the logs and alerts of the realized MAC spoofing, data injection, DoS, slave attacks were investigated, it was seen that the attacks were able to be detected successfully. For anomaly detection part of the study, k-NN and SVM GA techniques were found to be successful in detecting events