Biblio

The Industrial Internet expands the attack surface of industrial control systems(ICS), bringing cybersecurity threats to industrial controllers located in operation technology(OT) networks. Honeypot technology is an important means to detect network attacks. However, the existing honeypot system cannot simulate business logic and is difficult to resist highly concealed APT attacks. This paper proposes a high-simulation ICS security defense framework based on virtualization technology. The framework utilizes virtualization technology to build twins for protected control systems. The architecture can infer the execution results of control instructions in advance based on actual production data, so as to discover hidden attack behaviors in time. This paper designs and implements a prototype system and demonstrates the effectiveness and potential of this architecture for ICS security.

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework.

The industrial Internet of Things (IIoT) can facilitate industrial upgrading, intelligent manufacturing, and lean production. Industrial control system (ICS) is a vital support mechanism for many key infrastructures in the IIoT. However, natural defects in the ICS network security mechanism and the susceptibility of the programmable logic controller (PLC) program to malicious attack pose a threat to the safety of national infrastructure equipment. To improve the security of the underlying equipment in ICS, a model checking method based on timed automata is proposed in this work, which can effectively model the control process and accurately simulate the system state when incorporating time factors. Formal analysis of the ICS and PLC is then conducted to formulate malware detection rules which can constrain the normal behavior of the system. The model checking tool UPPAAL is then used to verify the properties by detecting whether there is an exception in the system and determine the behavior of malware through counter-examples. The chemical reaction control system in Tennessee-Eastman process is taken as an example to carry out modeling, characterization, and verification, and can effectively detect multiple patterns of malware and propose relevant security policy recommendations.

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.

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.

Modern industrial control systems (ICS) act as victims of cyber attacks more often in last years. These attacks are hard to detect and their consequences can be catastrophic. Cyber attacks can cause anomalies in the work of the ICS and its technological equipment. The presence of mutual interference and noises in this equipment significantly complicates anomaly detection. Moreover, the traditional means of protection, which used in corporate solutions, require updating with each change in the structure of the industrial process. An approach based on the machine learning for anomaly detection was used to overcome these problems. It complements traditional methods and allows one to detect signal correlations and use them for anomaly detection. Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation dataset was analyzed as example of industrial process. In the course of the research, correlations between the signals of the sensors were detected and preliminary data processing was carried out. Algorithms from the most common techniques of machine learning (decision trees, linear algorithms, support vector machines) and deep learning models (neural networks) were investigated for industrial process anomaly detection task. It's shown that linear algorithms are least demanding on computational resources, but they don't achieve an acceptable result and allow a significant number of errors. Decision tree-based algorithms provided an acceptable accuracy, but the amount of RAM, required for their operations, relates polynomially with the training sample volume. The deep neural networks provided the greatest accuracy, but they require considerable computing power for internal calculations.

In modern societies, critical services such as transportation, power supply, water treatment and distribution are strongly dependent on Industrial Control Systems (ICS). As technology moves along, new features improve services provided by such ICS. On the other hand, this progress also introduces new risks of cyber attacks due to the multiple direct and indirect dependencies between cyber and physical components of such systems. Performing rigorous security tests and risk analysis in these critical systems is thus a challenging task, because of the non-trivial interactions between digital and physical assets and the domain-specific knowledge necessary to analyse a particular system. In this work, we propose a methodology to model and analyse a System Under Test (SUT) as a data flow graph that highlights interactions among internal entities throughout the SUT. This model is automatically extracted from production code available in Programmable Logic Controllers (PLCs). We also propose a reachability algorithm and an attack diagram that will emphasize the dependencies between cyber and physical domains, thus enabling a human analyst to gauge various attack vectors that arise from subtle dependencies in data and information propagation. We test our methodology in a functional water treatment testbed and demonstrate how an analyst could make use of our designed attack diagrams to reason on possible threats to various targets of the SUT.

Cyber-attacks and intrusions in cyber-physical control systems are, currently, difficult to reliably prevent. Knowing a system's vulnerabilities and implementing static mitigations is not enough, since threats are advancing faster than the pace at which static cyber solutions can counteract. Accordingly, the practice of cybersecurity needs to ensure that intrusion and compromise do not result in system or environment damage or loss. In a previous paper [2], we described the Cyberspace Security Econometrics System (CSES), which is a stakeholder-aware and economics-based risk assessment method for cybersecurity. CSES allows an analyst to assess a system in terms of estimated loss resulting from security breakdowns. In this paper, we describe two new related contributions: 1) We map the Cyberspace Security Econometrics System (CSES) method to the evaluation and mitigation steps described by the NIST Guide to Industrial Control Systems (ICS) Security, Special Publication 800-82r2. Hence, presenting an economics-based and stakeholder-aware risk evaluation method for the implementation of the NIST-SP-800-82 guide; and 2) We describe the application of this tailored method through the use of a fictitious example of a critical infrastructure system of an electric and gas utility.

Industrial Control Systems (ICS) which among others are comprised of Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) are used to control industrial processes. ICS have now been connected to other Information Technology (IT) systems and have as a result become vulnerable to Advanced Persistent Threats (APT). APTs are targeted attacks that use zero-day attacks to attack systems. Current ICS security mechanisms fail to deter APTs from infiltrating ICS. An analysis of possible solutions to deter APTs was done. This paper proposes the use of Artificial Immune Systems to secure ICS from APTs.