Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems

PdGAT-ID: An intrusion detection method for industrial control systems based on periodic extraction and spatiotemporal graph attention

The need for detection, identification and prediction of anomalies emerged just a short time ago. This task brings multiple challenges that are mainly associated with recording and detection of novel attacks and influences. It draws attention of experts in network security and diagnostics of information and industrial systems. Detection of anomalies in dynamic dataflows largely determines the efficiency of management of computer network information security within information and industrial systems. At the same time, the technology of dynamic dataflow prediction is also very important for building systems intended to detect anomalies in data protection within industrial control systems. Any automated systems are based on available computer capabilities and advances in management theory, mathematical modeling and optimizations methods. Processes that occur in industrial automated systems and that are reflected in dataflows being observed constitute complex multi-component processes, thus making it more complicated to predict such processes. In this case, complex multi-component time series data should be predicted at different (short-term, medium-term and long-term) time scales. A neural network architecture matching the structure of the multi-component time series being predicted should be built for generating a multi-component prediction. It is proposed to decompose the complex multi-component time series into several basic components using the digital signal processing technology, i. e. to perform a preliminary structural analysis of multi-component time series within the observed range of all time series that reflect operation of the industrial control system. Separate predictions with different time horizon are formed for each basic component of the multi-component time series using the available neural network architecture and machine learning taking into account dynamic characteristics of the above components. Anomalies in the observed range of multiple time series that reflect operation of the industrial control system are detected (identified) through component-wise comparison of each component (resulted from the above preliminary digital processing) of any time series within the observed range of all time series, with each prediction of the relevant component of the above time series within the observed range of all time series. This approach that implies component-wise comparison will allow to detect anomalies within the range of observed time series of the industrial control system separately by their different dynamic characteristics, and thus will improve the efficiency of management of information security within information and industrial systems.

The rapid development of technology during the last decades has led to the integration of the network capabilities in the devices that are essential in the operation of Industrial Control Systems (ICS). Consequently, the attack surface of these assets has increased, putting at risk the nations that depend heavily on them for their continuity and functions. ICS physical and virtual testbeds are ideal platforms that can be used for cybersecurity research. Compared to physical testbeds, virtual testbeds are less expensive, safer to maintain, and more accessible, especially when access to research labs is restricted due to unforeseen circumstances such as COVID 19. Therefore, in this article, we propose VNWTS, a virtual water chlorination process for ICS cybersecurity analysis. VNWTS is composed of virtual components commonly found in physical ICS implementations. In addition, we implemented a set of attacks targeting the memory of the S7-1500 PLC and other VNWTS components such as level sensors, temperature sensors, and pumps. The results obtained from the experimentation phase show that the structure, configuration, and implementation of the VNWTS testbed can be used in ICS cybersecurity research.

Recent trends in automation technology lead to a rising exposition of industrial control systems (ICS) to new vulnerabilities. This requires the introduction of proper security approaches in this field. Prevalent in ICS is the use of access control. Especially in critical infrastructures, however, preventive security measures should be complemented by reactive ones, such as intrusion detection. Beginning from the characteristics of automation networks we outline the implications for a suitable application of intrusion detection in this field. On this basis, an approach for creation of self-learning anomaly detection for ICS protocols is presented. In contrast to other approaches, it takes all network data into account: flow information, application data, and the packet order. We discuss the challenges that have to be solved in each step of the network data analysis to identify future aspects of research towards learning normality in industrial control networks.

Industrial Control Systems (ICS), such as wastewater treatment systems, are frequently composed of hundreds of devices distributed over a large geographic area. While mobile applications have been used with good success in managing ICSs, traditional methods of distributing applications (e.g. app stores) are not well suited to the task of distributing ICS mobile applications, as the highly individualized and often proprietary individual components of ICSs have vastly different interfaces leading to a need to download hundreds of applications. We propose the No Effort Rapid Development (NERD) framework to address the challenges of in-field human-machine interface (HMI) discovery, provisioning, and co-evolution with related ICSs. Mobile cloud services offer the ability to simplify on-demand HMI distribution and operation of ICSs. NERD leverages existing ICS device-markers (e.g. QR-codes or RFID tags) for rapid cyber-physical discovery and provisioning of HMIs in the field. Device-markers have a very limited data capacity, and to achieve on-device storage of HMIs we propose using a compact data-driven domain specific language that emphasizes data sources and sinks between the HMI and ICS cloud based services. Our approach seamlessly links controls in the physical domain with specific resources in the digital domain, such as device-specific interfaces or ICS-wide control interfaces. We provide a quantitative evaluation of NERD and show how NERD can simplify the process of in-field discovery and provisioning of HMIs and related applications.

GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System

Cyber-physical infrastructures in industrial control systems, including critical infrastructure and manufacturing, heavily on cyber-physical infrastructures and embedded devices. As industrial control systems become more complex and network-centric, physical infrastructure becomes increasingly dispersed and vulnerable. In rapid growth within the competitive technology, we have seen an expressive increase in information security vulnerabilities against such systems, ranging from simple hacking tools to new generation intelligent attacks. With the growing reliance on industrial control networks and, of course, the growing number of attacks, the lack of cyber-security monitoring and forensic analysis of security incidents. Cyber-forensic analysis of ICS/SCADA systems is unlike standard forensic analysis of enterprise computer systems, the cyber-forensic specialist often has to be an expert in cyber-physical infrastructures systems, networks, and devices to determine where potential forensic evidence may be located. This paper discusses ICS/SCADA, typical attacks and vulnerabilities, problems with forensic analysis, and the development of forensic methodology/tools for such systems.

Ensuring the integrity of Ethernet-based networks is a challenging and constantly evolving domain. This problem is exacerbated for those operational technology (OT) networks supporting industrial control systems (ICS) since much of that equipment was originally designed to be on a network that was isolated and generally considered free of malefactors. Increasing pressure to bridge these systems with traditional information technology (IT) networks has introduced a bevy of new threats. In response, both academia and industry have responded with security solutions tailored to ICS environments. Deploying these protection systems often involves several configuration choices. While some of these choices are clear (e.g., block/enable protocol X) others are far more subjective (e.g. alert threshold == 3.43). Further complicating the situation, while often similar to IT networks, OT networks have unique challenges and characteristics that make the task of protecting them simultaneously more difficult and straight forward.Extant solutions for quantifying the relative security of intrusion detection systems fail to effectively support the operators of said systems with understanding the impact of various configuration changes. Further, they assume that the attacks are static and not subject to manipulation or alteration in the face of defenses. In this paper, we present a threat-based method for quantifying the relative impact of various security settings for intrusion detection systems (IDSs) within ICS environments. This method provides operational staff with a clear understanding of the relative impact of their settings and assumes that the attacks levied against them are dynamic. The model is described in detail, we apply the model to a synthetic data set, and discuss the inferences that can be made and what types of decisions they could be used to support.

The control instructions of industrial control systems are prone to threats such as unauthorized access and tampering during transmission and interaction, and access control is a fundamental method to protect data security. Due to the cyber-physical integration and availability constraints in industrial control systems, existing access control methods cannot be directly applied. In this paper, we propose an access control policy for control instructions based on the ciphertext policy attribute-based encryption (CP-ABE) under the availability constraints in industrial control systems. First, we analyze the abnormal behaviors of control instructions in process industrial monitoring systems, and model the attributes associated with field control business and integrate them into CP-ABE to achieve fine-grained access control and avoid non-compliant operations. Second, we adopt a trusted computing mechanism to protect the identity trustworthiness of the transmission node; the confidentiality of the transmitted control instruction is guaranteed by the negotiated symmetric key and the key authorization is realized by the CP-ABE. We further optimize the measuring frequency of the trusted measurement and the deployment policy of the access control method to guarantee business availability. Finally, we conduct formal analysis and experimental validation of the proposed method, and the results show that the proposed access control policy can prevent unauthorized access and non-compliant tampering by industrial control devices and achieve trustworthy delivery of control instructions with controlled computational complexity.

The implementation of automated regulatory control has been around since the middle of the last century through analog means. It has allowed engineers to operate the plant more consistently by focusing on overall operations and settings instead of individual monitoring of local instruments (inside and outside of a control room). A similar approach is proposed for cyber security, where current border-protection designs have been inherited from information technology developments that lack consideration of the high-reliability, high consequence nature of industrial control systems. Instead of an independent development, however, an integrated approach is taken to develop a holistic understanding of performance. This performance takes shape inside a multiagent design, which provides a notional context to model highly decentralized and complex industrial process control systems, the nervous system of critical infrastructure. The resulting strategy will provide a framework for researching solutions to security and unrecognized interdependency concerns with industrial control systems.

The integration of industrial control communication networks and the Internet into the Industrial Control System (ICS) increases their vulnerability to cyber-attacks, leading to devastating consequences. Traditional intrusion detection systems (IDS) mostly rely on predefined models and are trained mostly on specific cyber attacks, which means that traditional IDS cannot deal with unknown attacks In addition, most IDSs do not take into account the imbalance of ICS datasets, and therefore suffer from low accuracy and high false positives when used. In the article, we propose an NCO-double-layer DIFF_RF-OPFYTHON intrusion detection method for ICS, which consists of NCO modules, two-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the two-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Next, the known attacks will be classified by the OPFYTHON module into specific attacks according to the characteristics of the attack traffic. We use the NCO module to improve model inputs and improve model accuracy. The results show that the proposed method outperforms traditional intrusion detection methods such as XGboost and SVM. Detecting unknown attacks is also significant. The accuracy of the data set used in this article reaches 98.13%. The detection rate of unknown and known attacks reaches 98.21% and 95.1%, respectively. In this article, the basic modules of the classifier are shallow machine learning algorithms. This can be improved by using a more powerful neural network architecture. In the article, all detected unknown attacks are marked as one category. In further studies, the unknown attacks can be clustered and analyzed to further classify the detected unknown attacks. It is worth noting that the number of unknown attacks is only a small part, so it is difficult to classify unknown attacks using cluster analysis. Since training a more powerful neural network requires a lot of data, it is important to investigate how to train a new model when the number of samples belonging to that class is limited.

Recently, OT (operational technology) networks of industrial control systems have been combined with IT networks. Therefore, OT networks have inherited the vulnerabilities and attack paths existing in IT networks. Consequently, attacks on industrial control systems are increasing, and research on technologies combined with artificial intelligence for detecting attacks is active. Current research focuses on detecting attacks and improving the detection accuracy. Few studies exist on metrics that interpret anomaly detection results. Different analysis metrics are required depending on the characteristics of the industrial control system data used for anomaly detection and the type of attack they contain. We focused on the fact that industrial control system data are time series data. The accuracy and F1-score are used as metrics for interpreting anomaly detection results. However, these metrics are not suitable for evaluating anomaly detection in time series data. Because it is not possible to accurately determine the start and end of an attack, range-based performance metrics must be used. Therefore, in this study, when evaluating anomaly detection performed on time series data, we propose a range-based performance metric with an improved algorithm. The previously studied range-based performance metric time-series aware precision and recall (TaPR) evaluated all attacks equally. In this study, improved performance metrics were studied by deriving ambiguous instances according to the characteristics of each attack and redefining the algorithm of the TaPR metric. This study provides accurate assessments when performing anomaly detection on time series data and allows predictions to be evaluated based on the characteristics of the attack.

With the rapid development of technology, the scale of traffics in industrial control networks is increasing day by day. More malicious traffics brought terrible impacts on industrial areas. Modbus plays a momentous role in the communications of Industrial Control Systems (ICS), but it’s vulnerable to Denial of Service attacks(DoS). Traditional methods cannot perform well on fine-grained detection tasks which could contribute to locating targets of DoS and preventing the destruction. Considering the temporal locality and high dimension of malicious traffic, we proposed a Neural Network architecture named MODLSTM, which consists of three parts: input preprocessing, feature recoding, and traffic classification. By virtue of the design, MODLSTM can perform high-precision identification and fine-grained classification of DOS attacks in the Modbus/TCP-based system. To test our model’s performances, we conducted experiments on our traffic dataset collected from the industrial control network, and the models achieved excellent performances in comparison with previous work(accuracy increased by 0.74%). The results show that our proposed method has satisfactory abilities to detect DoS attacks related to Modbus, it could help to build a reliable firewall to address the DoS traffic in industrial environments.

Critical industrial infrastructures are currently facing increasing cyberspace threats in their underlying information and communication systems. The advanced monitoring, control, and management functionalities of the industrial systems firmly rely on the reliable and secure operations of the industrial control system (ICS) network. This article characterizes the ICS network traffic and presents a scalable and efficient solution for real-time ICS network traffic anomaly detection, considering various forms of ICS anomaly events. The events due to the cyberattacks, malicious operating behaviors, and network anomalies can be effectively detected without sophisticated computational requirements and retrieval of communication protocols. The proposed hybrid statistical-machine learning model integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the abnormal traffic patterns with low false omission rates. The proposed solution is extensively evaluated at a realistic ICS cyber–physical system (CPS) testbed, and the numerical results confirm its high detection accuracy and low computational complexity. Note to Practitioners—This article was motivated by the challenge of real-time anomaly detection in industrial cyber–physical systems (CPSs). The existing industrial control system (ICS) network anomaly detection solutions are generally carried out based on a single model based on the historian database and cannot dynamically classify the abnormal conditions in a real-time fashion. A novel hybrid statistical-machine learning model is developed that integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the anomalous events through traffic pattern analysis. The proposed anomaly detection solution can efficiently provide accurate detection for cyberattacks, malicious operating behaviors, and network anomalies while meeting the real-time requirements of ICS networks. The proposed solution can be deployed in the realistic ICS CPSs, e.g., the power generation system, gas pipeline systems, and urban railway transportation systems. The preliminary numerical results obtained from the ICS-CPS testbed suggested that it can provide high detection accuracy with low computational complexity and, hence, can be adopted with minimal deployment hurdles.

After investigating the conception and features of PLC (Programmable Logic Controller) and embedded system, in this paper the conception of a Quasi-PLC is proposed, which combine the advantages of PLC and embedded system. The architecture of Quasi-PLC and a Top-Down design method with collaboration design of software and hardware for Quasi-PLC embedded control system are researched then. Finally, setting the large-scale industrial valve control system as an example, the conception of Quasi-PLC and related technologies are used in industrial valve control system, and a satisfied application effect is obtained, which proves the rationality of the conception of Quasi-PLC and related technologies.