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2021-03-30
Tai, J., Alsmadi, I., Zhang, Y., Qiao, F..  2020.  Machine Learning Methods for Anomaly Detection in Industrial Control Systems. 2020 IEEE International Conference on Big Data (Big Data). :2333—2339.

This paper examines multiple machine learning models to find the model that best indicates anomalous activity in an industrial control system that is under a software-based attack. The researched machine learning models are Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Recurrent Neural Network classifiers built-in Python and tested against the HIL-based Augmented ICS dataset. Although the results showed that Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Long Short-Term Memory classification models have great potential for anomaly detection in industrial control systems, we found that Random Forest with tuned hyperparameters slightly outperformed the other models.

2020-05-08
Hansch, Gerhard, Schneider, Peter, Fischer, Kai, Böttinger, Konstantin.  2019.  A Unified Architecture for Industrial IoT Security Requirements in Open Platform Communications. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). :325—332.

We present a unified communication architecture for security requirements in the industrial internet of things. Formulating security requirements in the language of OPC UA provides a unified method to communicate and compare security requirements within a heavily heterogeneous landscape of machines in the field. Our machine-readable data model provides a fully automatable approach for security requirement communication within the rapidly evolving fourth industrial revolution, which is characterized by high-grade interconnection of industrial infrastructures and self-configuring production systems. Capturing security requirements in an OPC UA compliant and unified data model for industrial control systems enables strong use cases within modern production plants and future supply chains. We implement our data model as well as an OPC UA server that operates on this model to show the feasibility of our approach. Further, we deploy and evaluate our framework within a reference project realized by 14 industrial partners and 7 research facilities within Germany.

2020-01-21
Abdelghani, TSCHROUB.  2019.  Industrial Control Systems (Ics) Security in Power Transmission Network. 2019 Algerian Large Electrical Network Conference (CAGRE). :1–4.

The goal of this document is to provide knowledge of Security for Industrial Control Systems (ICS,) such as supervisory control and data acquisition (SCADA) which is implemented in power transmission network, power stations, power distribution grids and other big infrastructures that affect large number of persons and security of nations. A distinction between IT and ICS security is given to make a difference between the two disciplines. In order to avoid intrusion and destruction of industrials plants, some recommendations are given to preserve their security.

2019-05-09
Hata, K., Sasaki, T., Mochizuki, A., Sawada, K., Shin, S., Hosokawa, S..  2018.  Collaborative Model-Based Fallback Control for Secured Networked Control Systems. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. :5963-5970.

The authors have proposed the Fallback Control System (FCS) as a countermeasure after cyber-attacks happen in Industrial Control Systems (ICSs). For increased robustness against cyber-attacks, introducing multiple countermeasures is desirable. Then, an appropriate collaboration is essential. This paper introduces two FCSs in ICS: field network signal is driven FCS and analog signal driven FCS. This paper also implements a collaborative FCS by a collaboration function of the two FCSs. The collaboration function is that the analog signal driven FCS estimates the state of the other FCS. The collaborative FCS decides the countermeasure based on the result of the estimation after cyber-attacks happen. Finally, we show practical experiment results to analyze the effectiveness of the proposed method.

2017-04-03
Combs-Ford, Marcia.  2016.  Security Assessment of Industrial Control Supervisory and Process Control Zones. Proceedings of the 17th Annual Conference on Information Technology Education. :73–76.

With the discovery of the Stuxnet malware in June 2010, Industrial Control System (ICS) security has gained global attention and scrutiny. Due to the unique industrial control operating environment, standard information technology host-based defenses such as operating system upgrades are not always feasible. Therefore, ICS security strategies must rely upon layered network infrastructure and enclave boundary defenses. As ICS threats evolve, so too must ICS security practices and strategies. ICS security innovation rely upon understanding the effectiveness of established defenses and countermeasures. In an effort to evaluate the security effectiveness of ICS layered perimeter defenses, a Red Team security assessment was conducted on an ICS test network. This experiment offers insight to the effectiveness of ICS perimeter defenses by demonstrating the reduction of attack vectors, decreased adversarial network access, and perimeter network defenses are an effective ICS security strategy.