Security Of Control Systems Research Articles

인공지능 기반의 자동화된 통합보안관제시스템 모델 연구

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

A Hybrid Perspective on Threat Analysis and Activity-Based Attack Modeling for Strengthening Access Control in IoT

The rapid expansion of Internet of Things (IoT) devices has resulted in an unparalleled surge in the production of data and interconnectivity. Nevertheless, as IoT ecosystems become increasingly intricate, security concerns become of utmost importance, particularly in access control systems. The objective of this research is to improve the security of IoT access control by utilizing a hybrid model for analyzing threats and modeling attacks based on activities. This study has two primary objectives: a) A hybrid classification model is used to predict labels (attack or not) in binary classification with an impressive accuracy of 98.18%. b) Another hybrid classification model is employed to predict types of attacks in M2M communication, achieving a commendable accuracy of 90%. The primary goal is to create and assess a hybrid classification model for binary classification. This model will differentiate between regular system behavior and malicious attacks on access control schemes in the Internet of Things (IoT). The hybrid model, which combines the strengths of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks, achieves an exceptional accuracy rate of 98.18%. The model's high accuracy demonstrates its effectiveness in precisely detecting potential threats and minimizing false positives, thereby establishing a strong basis for improving access control security. The second objective focuses on the complex area of security, with the goal of categorizing distinct forms of attacks in Machine-to-Machine (M2M) communication within the Internet of Things (IoT) framework. The hybrid classification model, employing both GRU and LSTM networks, achieves a remarkable accuracy of 90%. This accomplishment showcases the model's aptitude in detecting and distinguishing different types of attacks, including Distributed Denial of Service (DDoS) and Man-in-the-Middle attacks. The hybrid model provides security professionals with valuable insights to proactively respond to diverse threats in M2M communication by accurately classifying attack types. This strengthens the overall security posture of IoT access control systems. Overall, this study offers a thorough and efficient combination of threat analysis and activity-based attack modeling to enhance access control in IoT. The obtained accuracies in binary classification and prediction of attack types highlight the practical usability of the suggested hybrid model, establishing a strong basis for improving the security of IoT access control systems against evolving cyber threats

Malicious Packet Detection Technology Using Reinforcement Learning

In the present day, the advancement of 5G and Internet of Things (IoT) technology has resulted in the interconnectedness of everyday objects through networks. However, attempts to exploit networked computers for malicious purposes continue to rise while the attacks utilizing malicious codes to compromise user information's confidentiality and integrity are becoming increasingly sophisticated and intelligent. To counter these evolving threats, researches have been conducted on a method to identify malicious network packets using a combination of security control systems and Artificial Intelligence (AI) technology, especially supervised learning. Unfortunately, the current cybersecurity control systems suffer from inefficiencies in terms of both manpower and cost. Moreover, the surge in remote work has created challenges in responding swiftly to security incidents. Furthermore, the existing AI technology based on supervised learning has limitations, particularly in detecting new variants of malicious code, and its accuracy in identifying malicious code depends heavily on the quantity and quality of available data. In light of these challenges, this study, reinforcement learning is employed to overcome the limitations of the original supervised learning-based malicious packet detection system, such as high dependency on training data and the failure to detect variant malicious packets. This research proposes a malicious packet detection technology capable of addressing new malicious packets or variant types effectively.

Enhancing Industrial Control System Security: An Isolation Forest-based Anomaly Detection Model for Mitigating Cyber Threats

Enhancing Industrial Control System Security: An Isolation Forest-based Anomaly Detection Model for Mitigating Cyber Threats

Risk assessment for industrial control systems based on asymmetric connection cloud and Choquet integral

Once industrial control systems are targeted by cyber-attacks, the consequences can be severe, including asset loss, environmental pollution, and public security risks. Risk assessment is an important way to ensure that industrial control systems operate efficiently, steadily and safely. The purpose of this paper is to develop a risk assessment model for industrial control systems based on asymmetric connection cloud and Choquet integral, which fully takes into account the fact that values of risk indicators are often fuzzy, random, asymmetrically distributed in finite intervals, and there are interactions among different indicators. To do so, we first establish a risk assessment index system to ensure the full reflection of availability, integrity, and confidentiality in the results of risk assessment for industrial control systems. Then we establish classification standards for each evaluation indicator based on the importance of assets, vulnerabilities, and threats in evaluating the risk of industrial control systems. Next we develop a risk assessment model based on asymmetric connection cloud and Choquet integral to determine the risk level of industrial control systems. In the following, an example is provided to demonstrate the feasibility and reliability of this proposed model. The experimental results have demonstrated a high level of credibility in assessing cyber-attacks by the proposed model, indicating its potential for analyzing the current security and risk posture of industrial control systems.

Industrial Control Systems Security Validation Based on MITRE Adversarial Tactics, Techniques, and Common Knowledge Framework

Industrial Control Systems (ICSs) have become the cornerstone of critical sectors like energy, transportation, and manufacturing. However, the burgeoning interconnectivity of ICSs has also introduced heightened risks from cyber threats. The urgency for robust ICS security validation has never been more pronounced. This paper provides an in-depth exploration of using the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) framework to validate ICS security. Although originally conceived for enterprise Information Technology (IT), the MITRE ATT&CK framework’s adaptability makes it uniquely suited to address ICS-specific security challenges, offering a methodological approach to identifying vulnerabilities and bolstering defence mechanisms. By zeroing in on two pivotal attack scenarios within ICSs and harnessing a suite of security tools, this research identifies potential weak points and proposes solutions to rectify them. Delving into Indicators of Compromise (IOCs), investigating suitable tools, and capturing indicators, this study serves as a critical resource for organisations aiming to fortify their ICS security. Through this lens, we offer tangible recommendations and insights, pushing the envelope in the domain of ICS security validation.

Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection.

Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability.

Design and implementation of computer network security detection and control system

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the systems design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.

Dual-Event-Triggered Intelligence Security Control for Multiagent Systems Against DoS Attacks With Applications in Mobile Robot Systems

The intelligence security control problem of multiagent systems in the presence of actuator faults is investigated against denial-of-service (DoS) attacks. Noticing that the DoS attacks usually make the actuator signal not available for distributed control protocols, a novel intelligence dual-eventtriggered control strategy is developed to eliminate the impact of DoS attacks. This control strategy includes two related event triggers respectively set on different channels, in which the second event-triggered condition is related to the triggering sequence of the previous event. Through the coordination between two nested event triggers set on different channels, the controller is avoided by being affected by wrong signals caused by DoS attacks. The developed distributed control protocol can not only guarantee the realization of the tracking control task but also ensure that no ‘Zeno behavior” occurs in the process of the event triggering. Moreover, the obtained results are applied to the mobile robot systems. Finally, a numerical simulation example and a mobile robot system application are given to verify the feasibility and effectiveness of the proposed control strategy.

Hysteretic quantised security control for PDE systems under replay attacks

ABSTRACT This article designs a quantised security controller for partial differential equation (PDE) systems under replay attacks. Initially, the Takagi – Sugeno fuzzy model is utilised to represent the considered nonlinear system and combines pointwise measurement and pointwise control methods to save the whole system’s costs. Moreover, to reduce network transmission pressure and simulate the network, hysteretic quantiser and replay attacks are considered. Furthermore, based on the Lyapunov direct method and the inequality technique, sufficient conditions to assure the stability of the closed-loop system are achieved. Ultimately, the effectiveness and superiority of the submitted control strategy are confirmed through numerical examples and comparative analysis.

Code generation for Security and Stability Control System based on extended reactive component

The Security and Stability Control System (SSCS) is developed to ensure the safe and stable operation of power grids, effectively mitigating failure propagation through emergency control strategies. However, due to the diversity, complexity, and region-specific nature of SSCS, the need for individualized development often arises, leading to time-consuming and error-prone in SSCS development. To address this challenge, we introduce ERC-Code, an automated development framework that empowers developers to prioritize design over implementation. ERC-Code includes extended reactive component modeling, correctness specifications, and model-to-code transformations. The extended reactive component offers the advantage of capturing the complex logic of SSCS at a high level of abstraction, including loops, branches and synchronizations. The correctness specifications prevent the creation of incomplete and invalid models, ensuring the robustness of the generated code. Additionally, the model-to-code engine automatically generates code from the extended reactive component. Our evaluation covers three key aspects: firstly, we assessed the effectiveness of ERC-Code in three real-world SSCS applications. We then conducted a comparison between the traces generated by ERC-Code and the ERC model. Finally, we conducted an experiment to evaluate developer productivity, drawing comparisons between ERC-Code and a code-centric approach. Our results exhibit promise, affirming ERC-Code’s applicability and code quality, along with enhanced productivity.

Techniques for Enhancing Security in Industrial Control Systems

Increasingly Industrial Control Systems (ICS) systems are being connected to the Internet to minimise the operational costs and provide additional flexibility. These control systems such as the ones used in power grids, manufacturing and utilities operate continually and have long lifespans measured in decades rather than years as in the case of Information Technology (IT) systems. Such industrial control systems require uninterrupted and safe operation. However, they can be vulnerable to a variety of attacks, as successful attacks on critical control infrastructures could have devastating consequences to the safety of human lives as well as a nation’s security and prosperity. Furthermore, there can be a range of attacks that can target ICS and it is not easy to secure these systems against all known attacks let alone unknown ones. In this paper, we propose a software enabled security architecture using Software Defined Networking (SDN) and Network Function Virtualisation (NFV) that can enhance the capability to secure industrial control systems. We have designed such an SDN/NFV enabled security architecture and developed a Control System Security Application (CSSA) in SDN Controller for enhancing security in ICS by achieving real time situational awareness and dynamic policy-driven decision making across the network infrastructure. In particular, CSSA can be used for establishing secure path for end-to-end communication between devices and also deal against certain specific attacks namely denial of service attacks, from unpatched vulnerable control system components and securing the communication flows from the legacy devices that do not support any security functionality. We also discuss how CSSA provides reliable paths for safety critical messages in control systems. We discuss the prototype implementation of the proposed architecture and the results obtained from our analysis.

Observer-Based Security Control for Nonlinear Suspension Systems With Cyber-Attacks and Hybrid-Triggered Scheme

Observer-Based Security Control for Nonlinear Suspension Systems With Cyber-Attacks and Hybrid-Triggered Scheme

Resilient Design of Networked Security Control for Active Suspension Systems via an Augmented Switching-Type Approach

Resilient Design of Networked Security Control for Active Suspension Systems via an Augmented Switching-Type Approach

Exploration of Quantum Cryptography Security Applications for Industrial Control Systems

Abstract The exploration of security applications of quantum cryptography for industrial control systems is a key research effort aimed at enhancing the security of industrial control systems through quantum cryptography. In this paper, we study the security threats faced by industrial control systems, including network attacks, data leakage, and system tampering, and propose to utilize quantum key distribution and quantum invisible state transfer algorithms to ensure the secure transmission of industrial control system data. The simulation test environment of the upper and lower computers of the industrial control system is built. The quantum encryption and decryption algorithms are deployed in the embedded environment and PCs to test the effectiveness of quantum cryptography to enhance the advanced encryption standard key scheme. The experimental results show that the quantum cryptography technology successfully realizes the encryption and decryption of data, and the total time consumed in the whole process is less than 61.8 seconds, which meets the requirements of a real-time industrial control system. Therefore, quantum cryptography is suitable for protecting field-level data in industrial control systems.

A Tree-based Scheme to Assess the Safety and Security for Industrial Control Systems

A Tree-based Scheme to Assess the Safety and Security for Industrial Control Systems

Transactive Energy System Deployment Over Insecure Communication Links

In this paper, the privacy and security issues associated with the transactive energy system (TES) deployment over insecure communication links are addressed. In particular, it is ensured that 1) individual agents’ bidding information is kept private throughout hierarchical market-based interactions; and 2) any extraneous data injection attack can be quickly and easily detected. An implementation framework is proposed to enable the cryptography-based enhancement of privacy and security for the deployment of any general hierarchical systems including TESs. Under the proposed framework, a unified cryptography-based approach is developed to achieve both privacy and security simultaneously. Specifically, privacy preservation is realized by an enhanced Paillier encryption scheme, where a block design is proposed to significantly improve computational efficiency. Attack detection is further achieved by an enhanced Paillier digital signature scheme, where a stamp-concatenation mechanism is proposed to enable detection of data replace and reorder attacks. Simulation results verify the effectiveness of the proposed cyber-resilient design for transactive energy systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by addressing the issues of cyber resiliency for practically deploying transactive energy system (TES) but it is also applicable to the problem of enhancing the privacy and security for any general hierarchical control systems. TES is an emerging control approach that engages energy suppliers and customers through market operations and uses the price to optimally allocate energy resources. While it has been shown to be promising for power system applications, the underlying market-based interactions raise significant concerns of privacy (data leakage) and security (data tampering). However, existing TES works only focus on the coordination mechanism instead of privacy and security issues. This paper proposes a new cryptography-based TES design for practical deployment. Specifically, to protect privacy, individual supply and demand amounts to be exchanged are all encrypted in a particular way such that the original amounts cannot be inferred from the encrypted amounts, while the desired computation for setting the market clearing price can be carried out over the encrypted amounts, thus generating an encrypted result which, when decrypted, matches that of the same computation over the original amounts. To achieve security, for each exchanged data, its sender generates a particular digital signature which is exchanged together with the data. This enables the receiver to automatically detect the integrity by checking whether a mathematical relationship holds for the pair of data and signature. In our future research, we will investigate more challenging scenarios where some suppliers and customers themselves could be corrupted and purposely submit distorted amounts.

Scrutinizing Security in Industrial Control Systems: An Architectural Vulnerabilities and Communication Network Perspective

Scrutinizing Security in Industrial Control Systems: An Architectural Vulnerabilities and Communication Network Perspective

Research and application of power marketing operation safety control technology

In order to improve the safety and efficiency of power marketing operation, the power marketing operation safety control technology is studied and applied. Through in-depth analysis of the connotation of power marketing and power marketing operation safety management and control technology, combined with advanced technology methods, the design strategy of security management and control system platform is put forward. It is hoped that this study can provide strong technical support and solutions for the operation security control of the power market.

Research on the Construction of Campus Security Prevention and Control System Based on Internet of Things Technology

Abstract Campus safety is related to the healthy growth of students, and the research focus of this paper is to explore the impact of Internet of Things (IoT) technology on campus safety prevention and control. Based on the correlation between ISM and DEMATEL methods, this paper constitutes an integrated DEMATEL-ISM model by using neighbor matrix and reachable matrix to transform each other. It constructs a risk factor model for campus security prevention and control evaluation based on Internet of Things technology according to the modeling steps after soliciting experts’ suggestions through questionnaires. Combined with the SEM model to estimate students’ campus risk identification ability, the path index was used to construct the college security awareness score index to explore the relationship between latent variables (influencing factors) and observed variables (risk identification ability). The results show that every 1% increase in the safety climate of the social environment will lead to the rise of 0.58%, 0.20%, and 0.52% in the on-campus environment factor, the campus safety management team building factor, and the students’ literacy factor, respectively. A 0.17% increase in the on-campus environment will occur with every 1% increase in students’ literacy in higher education. This study contributes to a deeper understanding of the role of IoT technology in enhancing campus security prevention and control. It can provide deep research references for subsequent risk grading evaluation, risk warning prevention and control, and supervision in campus risk management.