Industrial Control Systems Environment Research Articles

An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

With the rapid development of intelligent manufacturing and Industrial Internet of Things, many industrial control systems have high requirements for the security of the system itself. Failures of industrial control equipment will cause abnormal operation of industrial control equipment and waste of resources. It is very meaningful to detect and identify potential equipment abnormalities and failures in time and implement effective fault tolerance strategies. In the Industrial Internet of Things environment, the instructions and parameters of industrial control equipment often change due to changes in actual requirements. However, it is impractical to customize the learning method for each parameter value. Aiming at the problem, this paper proposes a fault diagnosis model based on ensemble learning and proposes a method of updating voting weights based on dynamic programming to assist decision-making. This method is based on Bagging strategy and combined with dynamic programming voting weight adjustment method to complete fault type prediction. Finally, this paper uses different loads as dynamic conditions; the diagnostic capability of the Bagging-based fault diagnosis integrated model in a dynamically changing industrial control system environment is verified by experiments. The fault diagnosis model of industrial control equipment based on ensemble learning effectively improves the adaptive ability of the model and makes the fault diagnosis framework truly intelligent. The voting weight adjustment method based on dynamic programming further improves the reliability of voting.

Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment

Technology has become an integral part of contemporary society. The current transition from an industrial society to an information society is accompanied by the implementation of new technologies in every part of human activity. Increasing pressure to apply ICT in critical infrastructure resulted in the creation of new vulnerabilities. Traditional safety approaches are ineffective in a considerable number of cases. Therefore, machine learning another evolutionary step that provides robust solutions for extensive and sophisticated systems. The article focuses on cybersecurity research for industrial control systems that are widely used in the field of critical information infrastructure. Moreover, cybernetic protection for industrial control systems is one of the most important security types for a modern state. We present an adaptive solution for defense against cyber-attacks, which also consider the specifics of the industrial control systems environment. Moreover, the experiments are based on four machine learning algorithms (artificial neural network, recurrent neural network LSTM, isolation forest, and algorithm OCSVM). The proposed anomaly detection system utilizes multiple techniques and processes as preprocessing techniques, optimization techniques, and processes required for result interpretation. These procedures allow the creation of an adaptable and robust system that meets the need for industrial control systems.

A three-tiered intrusion detection system for industrial control systems

Abstract This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F-measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.

Building a Cybersecurity Culture in the Industrial Control System Environment

Cybersecurity in industrial control system environments has become a significant concern and is even more relevant in the context of critical infrastructures where control system disruption could have a profound impact on health, safety and the environment. This makes this type of system a major target for malicious activities. Notwithstanding an organization’s interest in protecting its industrial control systems against cyber-attacks, the implementation of security measures, whether technical, organizational or human, still faces resistance and is often seen as a constraint. Using the best technology to protect industrial control systems makes no sense if persons with access do not act attentively and protectively. Technical and human cybersecurity measures are intrinsically linked, and it is essential that all persons with access to these systems are fully aware of the inherent cyber risks. Organizations must also act so that staff receive appropriate training on how to keep systems continuously protected against cyber-attack when carrying out their daily tasks. These educational processes can contribute to building an effective cybersecurity culture fully reflective of management and staff attitudes, so that the availability, integrity and confidentiality of information in industrial control systems can be assured.

A Efficient Network Security Management Model in Industrial Control System Environments

A Efficient Network Security Management Model in Industrial Control System Environments

A security-hardened appliance for implementing authentication and access control in SCADA infrastructures with legacy field devices

Considerable progress has been made with regard to securing industrial control systems. However, security challenges remain for field devices, and these challenges are compounded by the presence of legacy field devices. This paper describes the design, implementation and performance of a security-hardened, bolt-on, security appliance for legacy field devices. The approach uses a microkernel-based architecture and employs Bloom filters to implement challenge-response authentication and role-based access control for in an in-line field device security pre-processor. The microkernel-based architecture isolates network-interacting software from security-enforcing components, reducing the size of the trusted computing base of the device. Bloom filters provide a fast and constant access time solution for authentication and authorization checks. An analysis of the impact of Bloom filter false positive rates is provided, and it is shown that the false positive rates can be made arbitrarily low. Experimental results are also presented for a prototype device. Security-related computations on the pre-processor take less than one millisecond to perform, indicating that the prototype and the underlying approach are well-suited to a variety of industrial control system environments. Penetration tests demonstrate that the device is robust to attack, except for certain denial-of-service attacks.

A model of distributed key generation for industrial control systems

The cyber-security of industrial control systems (ICS) is gaining high relevance due to the impact of industrial system failures on the citizen life. There is an urgent need for the consideration of security in their design, and for the analysis of the related vulnerabilities and potential threats. The high exposure of industrial critical infrastructure to cyber-threats is mainly due to the intrinsic weakness of the communication protocols used to control the process network. The peculiarities of the industrial protocols (low computational power, large geographical distribution, near to real-time constraints) make hard the effective use of traditional cryptographic schemes and in particular the implementation of an effective key management infrastructure supporting a cryptographic layer. In this paper, we describe a “model of distributed key generation for industrial control systems” we have recently implemented. The model is based on a known Distributed Key Generator protocol we have adapted to an industrial control system environment and to the related communication protocol (Modbus). To validate in a formal way selected security properties of the model, we introduced a Petri Nets representation. This representation allows for modeling attacks against the protocol and understanding some potential weaknesses of its implementation in the industrial control system environment.

Focus on test and measurement

Focus on test and measurement