Most proposals for securing control systems are heuristic in nature, and while they increase the protection of their target, the security guarantees they provide are unclear. This paper proposes a new way of modeling the security guarantees of a Cyber-Physical System (CPS) against arbitrary false command attacks. As our main case study, we use the most popular testbed for control systems security. We first propose a detailed formal model of this testbed and then show how the original configuration is vulnerable to a single-actuator attack. We then propose modifications to the control system and prove that our modified system is secure against arbitrary, single-actuator attacks.

The term "Internet of things (IoT) security" refers to the software industry concerned with protecting the IoT and connected devices. Internet of Things (IoT) is a network of devices connected with computers, sensors, actuators, or users. In IoT, each device has a distinct identity and is required to automatically transmit data over the network. Allowing computers to connect to the Internet exposes them to a number of major vulnerabilities if they are not properly secured. IoT security concerns must be monitored and analyzed to ensure the proper working of IoT models. Protecting personal safety while ensuring accessibility is the main objective of IoT security. This article has surveyed some of the methods and techniques used to secure data. Accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve are the assessment metrics utilized to compare the performance of the existing techniques. Further the utilization of machine learning algorithms like Decision Tree, Random Forest, and ANN tests have resulted in an accuracy of 99.4\%. Despite the results, Random Forest (RF) performs significantly better. This study will help to gain more knowledge on the smart home automation and its security challenges.

Since criminality is rising in the 21st century, people want to secure their property and belongings. So, everyone in this situation needs a secure system with cutting-edge technology. Therefore, a person may go out without worries. This project aims to acquire a home security system that can apply a phone call to the client’s GSM (Global System for Mobile) cell phone device and send a message in the shortest amount of time. Our Home security system has been followed by the latest technology at a low cost. In this study, we used the PIR (Passive Infra-Red) movement sensor, the Arduino sensor as the core for movement identification, and the GSM module for dialing the system user, which was used to develop the hardware for this system. This framework uses the Arduino IDE for Arduino and Putty for participating in programming analysis in the GSM unit. The PIR sensor has a crucial function used in this system for the security of any unauthorized individuals and automatically generates calls when neighboring circles intrude and are detected by the PIR sensor. The Integrated Home Safety framework can promptly examine and sense a human’s movement.

Most proposals for securing control systems are heuristic in nature, and while they increase the protection of their target, the security guarantees they provide are unclear. This paper proposes a new way of modeling the security guarantees of a Cyber-Physical System (CPS) against arbitrary false command attacks. As our main case study, we use the most popular testbed for control systems security. We first propose a detailed formal model of this testbed and then show how the original configuration is vulnerable to a single-actuator attack. We then propose modifications to the control system and prove that our modified system is secure against arbitrary, single-actuator attacks.

The increasing complexity and interconnectedness of Industrial Control Systems (ICSs) necessitate the integration of safety and security measures. Ensuring the protection of both personnel and critical assets has become a necessity. As a result, an integrated risk assessment approach is essential to comprehensively identify and address potential hazards and vulnerabilities. However, the data sources needed for an integrated risk assessment comes in many forms. In this context, Automation Markup Language (AutomationML or AML) emerges as a valuable solution to facilitate data exchange and integration in the risk assessment process. The benefits of utilizing AML include improved interoperability, enhanced documentation, and seamless collaboration between stakeholders. A model, filled with information relevant to integrated risk assessment, is developed to illustrate the effectiveness of AML. Ultimately, this paper showcases how AML serves as a valuable information model in meeting the growing need for comprehensive safety and security risk assessment in ICSs.

Recently, the manufacturing industry is changing into a smart manufacturing era with the development of 5G, artificial intelligence, and cloud computing technologies. As a result, Operational Technology (OT), which controls and operates factories, has been digitized and used together with Information Technology (IT). Security is indispensable in the smart manu-facturing industry as a problem with equipment, facilities, and operations in charge of manufacturing can cause factory shutdown or damage. In particular, security is required in smart factories because they implement automation in the manufacturing industry by monitoring the surrounding environment and collecting meaningful information through Industrial IoT (IIoT). Therefore, in this paper, IIoT security proposed in 2022 and recent technology trends are analyzed and explained in order to understand the current status of IIoT security technology in a smart factory environment.

The Internet of Things, or IoT, is a paradigm in which devices interact with the physical world through sensors and actuators, while still communicating with other computers over various types of networks. IoT devices can be found in many environments, often in the hands of non-technical users. This presents unique security concerns, since compromised devices can be used not only for typical objectives like network footholds, but also to cause harm in the real world (for instance, by unlocking the door to a house or changing safety configurations in an industrial control system). This work in progress paper presents a series of laboratory exercises under development at a large Midwestern university that introduces undergraduate cyber security engineering students to the Internet of Things and its (in)security considerations. The labs will be part of a 400-level technical elective course offered to cyber security engineering majors. The design of the labs has been grounded in the experiential learning process. The concepts in each lab module are couched in hands-on activities and integrate real world problems into the laboratory environment. The laboratory exercises are conducted using an Internet testbed and a combination of actual IoT devices and virtualized devices to showcase various IoT environments, vulnerabilities, and attacks.

Anomaly and intrusion detection in industrial cyber-physical systems has attracted a lot of attention in recent years. Deep learning techniques that require huge datasets are actively researched nowadays. The great challenge is that the real data on such systems, especially security-related data, is confidential, and a methodology for dataset generation is required. In this paper, the authors consider this challenge and introduce the methodology of dataset generation for research on the security of industrial water treatment facilities. The authors describe in detail two stages of the proposed methodology: the definition of a technological process and creating a testbed. The paper ends with a conclusion and future work prospects.

The paper proposes an algorithm for verifying the authenticity of automated process control system actuators based on the HART standard, which can act as the main or additional measure of protection against threats to the integrity of the system. The principle of operation of the HART standard is considered, a theoretical algorithm is given, additional technical solutions that increase its reliability are considered, as well as scenarios of possible attacks.

While the introduction of cyber physical systems (CPS) into society is progressing toward the realization of Society 5.0, the threat of cyberattacks on IoT devices(IoT actuators) that have actuator functions to bring about physical changes in the real world among the IoT devices that constitute the CPS is increasing. In order to prepare for unauthorized control of IoT actuators caused by cyberattacks that are evolving daily, such as zero-day attacks that exploit unknown vulnerabilities in programs, it is an urgent issue to strengthen the CPS, which will become the social infrastructure of the future. In this paper, I explain, in particular, the security requirements for IoT actuators that exert physical action as feedback from cyberspace to the physical space, and a security framework for control that changes the real world, based on changes in cyberspace, where attackers are persistently present. And, I propose a security scheme for IoT actuators that integrates a new concept of security known as Zero Trust, as the Zero Trust IoT Security Framework (ZeTiots-FW).

The rapid development in IT and OT system makes interactions among themselves and with humans immerse in the information flows from the physical to cyberspace. The traditional view of cyber-security faces challenges of deliberate cyber-attacks and unpredictable failures. Hence, cyber resilience is a fundamental property that protects critical missions. In this paper, we presented a mission-oriented security framework to establish and enhance cyber-resilience in design and action. The definition of mission-oriented security is given to extend CIA metrics of cyber-security, and the process of mission executions is analyzed to distinguish the critical factors of cyber-resilience. The cascading failures in inter-domain networks and false data injection in the cyber-physical system are analyzed in the case study to demonstrate how the mission-oriented security framework can enhance cyber resilience.

Provable Security - This paper studies provable security guarantees for cyber-physical systems (CPS) under actuator attacks. Specifically, we consider safety for CPS and propose a new attack-detection mechanism based on a zeroing control barrier function (ZCBF) condition. To reduce the conservatism in its implementation, we design an adaptive recovery mechanism based on how close the state is to violating safety. We show that the attack-detection mechanism is sound, i.e., there are no false negatives for adversarial attacks. Finally, we use a Quadratic Programming (QP) approach for online recovery (and nominal) control synthesis. We demonstrate the effectiveness of the proposed method in a case study involving a quadrotor with an attack on its motors.

Intelligent transportation systems, such as connected vehicles, are able to establish real-time, optimized and collision-free communication with the surrounding ecosystem. Introducing the internet of things (IoT) in connected vehicles relies on deployment of massive scale sensors, actuators, electronic control units (ECUs) and antennas with embedded software and communication technologies. Combined with the lack of designed-in security for sensors and ECUs, this creates challenges for security engineers and architects to identify, understand and analyze threats so that actions can be taken to protect the system assets. This paper proposes a novel STRIDE-based threat model for IoT sensors in connected vehicle networks aimed at addressing these challenges. Using a reference architecture of a connected vehicle, we identify system assets in connected vehicle sub-systems such as devices and peripherals that mostly involve sensors. Moreover, we provide a prioritized set of security recommendations, with consideration to the feasibility and deployment challenges, which enables practical applicability of the developed threat model to help specify security requirements to protect critical assets within the sensor network.

Security is an essential requirement of Industrial Control System (ICS) environments and its underlying communication infrastructure. Especially the lowest communication level within Supervisory Control and Data Acquisition (SCADA) systems - the field level - commonly lacks security measures.Since emerging wireless technologies within field level expose the lowest communication infrastructure towards potential attackers, additional security measures above the prevalent concept of air-gapped communication must be considered.Therefore, this work analyzes security aspects for the wireless communication protocol IO-Link Wireless (IOLW), which is commonly used for sensor and actuator field level communication. A possible architecture for an IOLW safety layer has already been presented recently [1].In this paper, the overall attack surface of IOLW within its typical environment is analyzed and attack preconditions are investigated to assess the effectiveness of different security measures. Additionally, enhanced security measures are evaluated for the communication systems and the results are summarized. Also, interference of security measures and functional safety principles within the communication are investigated, which do not necessarily complement one another but may also have contradictory requirements.This work is intended to discuss and propose enhancements of the IOLW standard with additional security considerations in future implementations.

In this paper, we investigate the conditions for the existence of dynamically undetectable attacks and perfectly undetectable attacks. Then we provide a quantitative measure on the security for discrete-time linear time-invariant (LTI) systems under both actuator and sensor attacks based on undetectability. Finally, the computation of proposed security index is reduced to a min-cut problem for the structured systems by graph theory. Numerical examples are provided to illustrate the theoretical results.

This publication deals with the robust attitude stabilization of a quadrotor subject to stealthy actuator attacks. Based first on the nonlinear model of the system, the sector non-linearity approach will be applied in order to deduce a polytopic Takagi-sugeno model. In parallel, a polytopic fuzzy T-S modeling of the data-deception malicious attacks (time-varying parameters) is presented. After some mathematical development, it will be shown that our original nonlinear system subject to stealthy actuator attacks can be represented as an uncertain polytopic T-S system. Based on this latest model, basic concepts for attitude stabilization will be used to implement the control law. The stabilization conditions will be given in terms of Linear Matrix Inequalities (LMIs) deduced from a classical Lyapunov approach. In order to highlight the efficiency of the proposed approach, simulation results will be given.

This study addresses the coordination issue of multi-agent systems under complicated actuator faults and cyber attacks. Distributed fault-tolerant design is developed with the estimated and output neighboring information in decentralized estimation observer. Criteria of reaching the exponential coordination of multi-agent systems with cyber attacks is obtained with average dwelling time and chattering bound method. Simulations validate the efficiency of the anti-attack fault-tolerant design.

The emergence of CPSs leads to modernization of critical infrastructures and improving flexibility and efficiency from one point of view. However, from another point of view, this modernization has subjected them to cyber threats. This paper provides a modeling approach for evaluating the security of CPSs. The main idea behind the presented model is to study the attacker and the system behaviors in the penetration and attack phases with exploiting some defensive countermeasures such as redundant components and attack detection strategies. By using the proposed approach, we can investigate how redundancy factor of sensors, controllers and actuators and intrusion detection systems can improve the system security and delay the system security failure.

This demonstration presents an internet of things device (thermostat), whose security is enforced by a secure element (smartcard) running TLS server, and using Virtual Input/Ouput technology. The board comprises a Wi-Fi system on chip (SoC), a micro-controller managing sensor (temperature probe) and actuator (relay), and a javacard. All device messages are sent/received over TLS, and processed by the secure element. Some of them are exported to micro-controller in clear form, which returns a response, sent over TLS by the smartcard.

Machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial machinery. The use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems.

In control systems, the operation of the system after an incident occurs is important. This paper proposes to design a whitelist model that can detect anomalies and identify locations of anomalous actuators using finite automata during multiple actuators attack. By applying this model and comparing the whitelist model with the operation data, the monitoring system detects anomalies and identifies anomaly locations of actuator that deviate from normal operation. We propose to construct a whitelist model focusing on the order of the control system operation using binary search trees, which can grasp the state of the system when anomalies occur. We also apply combinatorial compression based on BDD (Binary Decision Diagram) to the model to speed up querying and identification of abnormalities. Based on the model designed in this study, we aim to construct a secured control system that selects and executes an appropriate fallback operation based on the state of the system when anomaly is detected.

Cyber Physical Systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical Systems are a crucial component of Internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the Internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective Network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection (NSRM-AD) using deep learning model is proposed. The security levels of the proposed model are contrasted with the proposed model and the results represent that the proposed model performance is accurate

Security attacks on sensor data can deceive a control system and force the physical plant to reach an unwanted and potentially dangerous state. Therefore, attack detection mechanisms are employed in cyber-physical control systems to detect ongoing attacks, the most prominent one being a threshold-based anomaly detection method called CUSUM. Literature defines the maximum impact of stealth attacks as the maximum deviation in the plant’s state that an undetectable attack can introduce, and formulates it as an optimization problem. This paper proposes an optimization-based attack with different saturation models, and it investigates how the attack duration significantly affects the impact of the attack on the state of the plant. We show that more dangerous attacks can be discovered when allowing saturation of the control system actuators. The proposed approach is compared with the geometric attack, showing how longer attack durations can lead to a greater impact of the attack while keeping the attack stealthy.

Cyber-Physical Systems (CPSs), a class of complex intelligent systems, are considered the backbone of Industry 4.0. They aim to achieve large-scale, networked control of dynamical systems and processes such as electricity and gas distribution networks and deliver pervasive information services by combining state-of-the-art computing, communication, and control technologies. However, CPSs are often highly nonlinear and uncertain, and their intrinsic reliance on open communication platforms increases their vulnerability to security threats, which entails additional challenges to conventional control design approaches. Indeed, sensor measurements and control command signals, whose integrity plays a critical role in correct controller design, may be interrupted or falsely modified when broadcasted on wireless communication channels due to cyber attacks. This can have a catastrophic impact on CPS performance. In this paper, we first conduct a thorough analysis of recently developed secure and resilient control approaches leveraging the solid foundations of adaptive control theory to achieve security and resilience in networked CPSs against sensor and actuator attacks. Then, we discuss the limitations of current adaptive control strategies and present several future research directions in this field.

The security control problem of cyber-physical system (CPS) under actuator attacks is studied in the paper. Considering the strict-feedback cyber-physical systems with external disturbance, a security control scheme is proposed by combining backstepping method and super-twisting sliding mode technology when the transmission control input signal of network layer is under false data injection(FDI) attack. Firstly, the unknown nonlinear function of the CPS is identified by Radial Basis Function Neural Network. Secondly, the backstepping method and super-twisting sliding mode algorithm are combined to eliminate the influence of actuator attack and ensure the robustness of the control system. Then, by Lyapunov stability theory, it is proved that the proposed control scheme can ensure that all signals in the closed-loop system are semi-global and ultimately uniformly bounded. Finally, the effectiveness of the proposed control scheme is verified by the inverted pendulum simulation.