Biblio

In order to improve the security and anti-interference ability of industrial control system, this paper proposes an improved industrial neural network defense method based on the PCA dimension reduction and the improved deep neural network. Firstly, the proposed method reduces the dimensionality of the industrial data using the dimension reduction theory of principal component analysis (PCA). Then the deep neural network extracts the features of the network. Finally, the softmax classifier classifies industrial data. Experiment results show that compared with unintegrated algorithm, this method achieves higher recognition accuracy and has great application potential.

Affected by the inherent ideas like "Focus on Function Realization, Despise Security Protection", there are lots of hidden threats in the industrial control system of wind farms (ICS-WF), such as unreasonable IP configuration, failure in virus detection and killing, which are prone to illegal invasion and attack from the cyberspace. Those unexpected unauthorized accesses are quite harmful for the stable operation of the wind farms and regional power grid. Therefore, by investigating the current security situation and needs of ICS-WF, analyzing the characteristics of ICS-WF’s architecture and internal communication, and integrating the ideas of the classified protection of cybersecurity, this paper proposes a new customized cybersecurity strategy for ICS-WF based on the barrel theory. We also introduce an new anomalous intrusion detection technology for ICS-WF, which is developed based on statistical models of wind farm network characteristics. Finally, combined all these work with the network security offense and defense drill in the industrial control safety simulation laboratory of wind farms, this research formulates a three-dimensional comprehensive protection solution for ICS-WF, which significantly improves the cybersecurity level of ICS-WF.

The Industrial Internet of Things is expected to attract significant investments for Industry 4.0. In this new environment, the blockchain has immediate potential in industrial applications, providing unchanging, traceable and auditable access control. However, recent work and present in blockchain literature are based on a cloud infrastructure that requires significant investments. Furthermore, due to the placement and distance of the cloud infrastructure to industrial control devices, such approaches present a communication latency that can compromise the strict deadlines for accessing and communicating with this device. In this context, this article presents a blockchain-based access control architecture, which is deployed directly to edge devices positioned close to devices that need access control. Performance assessments of the proposed approach were carried out in practice in an industrial mining environment. The results of this assessment demonstrate the feasibility of the proposal and its performance compared to cloud-based approaches.

With their role as an integral part of its infrastructure, Industrial Control Systems (ICS) are a vital part of every nation's industrial development drive. Despite several significant advancements - such as controlled-environment agriculture, automated train systems, and smart homes, achieved in critical infrastructure sectors through the integration of Information Systems (IS) and remote capabilities with ICS, the fact remains that these advancements have introduced vulnerabilities that were previously either nonexistent or negligible, one being Remote Access Trojans (RATs). Present RAT detection methods either focus on monitoring network traffic or studying event logs on host systems. This research's objective is the detection of RATs by comparing actual utilized system capacity to reported utilized system capacity. To achieve the research objective, open-source RAT detection methods were identified and analyzed, a GAP-analysis approach was used to identify the deficiencies of each method, after which control algorithms were developed into source code for the solution.

Cyber-attacks on our Nation's Critical Infrastructure are growing. In this research, a Cyber Threat Intelligence (CTI) framework is proposed, developed, and tested. The results of the research, using 5 different simulated attacks on a dataset from an Industrial Control System (ICS) testbed, are presented with the extracted IOCs. The Bagging Decision Trees model showed the highest performance of testing accuracy (94.24%), precision (0.95), recall (0.93), and F1-score (0.94) among the 9 different machine learning models studied.

Algorithms for unsupervised anomaly detection have proven their effectiveness and flexibility, however, first it is necessary to calculate with what ratio a certain class begins to be considered anomalous by the autoencoder. For this reason, we propose to conduct a study of the efficiency of autoencoders depending on the ratio of anomalous and non-anomalous classes. The emergence of high-speed networks in electric power systems creates a tight interaction of cyberinfrastructure with the physical infrastructure and makes the power system susceptible to cyber penetration and attacks. To address this problem, this paper proposes an innovative approach to develop a specification-based intrusion detection framework that leverages available information provided by components in a contemporary power system. An autoencoder is used to encode the causal relations among the available information to create patterns with temporal state transitions, which are used as features in the proposed intrusion detection. This allows the proposed method to detect anomalies and cyber attacks.

In order to solve the problem of quantitative assessment of information security risks in industrial control systems, this paper proposes a method of information security risk assessment for industrial control systems based on modular hybrid genetic algorithm. Combining with the characteristics of industrial control systems, the use of hybrid genetic algorithm evidence theory to identify, evaluate and assess assets and threats, and ultimately come to the order of the size of the impact of security threats on the specific industrial control system information security. This method can provide basis for making decisions to reduce information security risks in the control system from qualitative and quantitative aspects.

The paper gives an overview of the ICS security and focuses on Control Systems. Use of internet had security challenges which led to the development of ICS which is designed to be dependable and safe. PCS, DCS and SCADA all are subsets of ICS. The paper gives a description of the developments in the ICS security and covers the most interesting work done by researchers. The paper also provides research information about the parameters on which a remotely executed cyber-attack depends.

The rapid development of the Industry 4.0 initiative highlights the problems of Cyber-security of Industrial Computer Systems and, following global trends in Cyber Defense, the implementation of Artificial Intelligence instruments. The authors, having certain achievement in the implementation of Artificial Intelligence tools in Cyber Protection of Information Systems and, more precisely, creating and successfully experimenting with a hybrid model of Intrusion Detection and Prevention System (IDPS), decided to study and experiment with the possibility of applying a similar model to Industrial Control Systems. This raises the question: can the experience of applying Artificial Intelligence methods in Information Systems, where this development went beyond the experimental phase and has entered into the real implementation phase, be useful for experimenting with these methods in Industrial Systems.

This paper focuses on the typical business scenario of intelligent factory, it includes the manufacturing process, carries out hierarchical security protection, forms a full coverage industrial control security protection network, completes multi-means industrial control security direct protection, at the same time, it utilizes big data analysis, dynamically analyzes the network security situation, completes security early warning, realizes indirect protection, and finally builds a self sensing and self-adjusting industrial network security protection system It provides a reliable reference for the development of intelligent manufacturing industry.