Biblio

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 (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

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.

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.

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.

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 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.

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.

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.

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.

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.

The paper considers the issue of recurrent neural networks applicability for detecting industrial process anomalies to detect intrusion in Industrial Control Systems. Cyberattack on Industrial Control Systems often leads to appearing of anomalies in industrial process. Thus, it is proposed to detect such anomalies by forecasting the state of an industrial process using a recurrent neural network and comparing the predicted state with actual process' state. In the course of experimental research, a recurrent neural network with one-dimensional convolutional layer was implemented. The Secure Water Treatment dataset was used to train model and assess its quality. The obtained results indicate the possibility of using the proposed method in practice. The proposed method is characterized by the absence of the need to use anomaly data for training. Also, the method has significant interpretability and allows to localize an anomaly by pointing to a sensor or actuator whose signal does not match the model's prediction.

Cyber-physical systems, which are ubiquitous in modern critical infrastructure, oftentimes rely on sending actuation commands and sensor measurements over a network, subjecting this information to potential man-in-the-middle attacks. These attacks can take the form of denial of service attacks or integrity attacks. Previous approaches at ensuring the resiliency of the overall control system against these types of attacks have leveraged functional redundancy in the system, including resilient estimation and reconfigurable control. However, these approaches are only able to ensure resiliency up to a particular subset of the actuator commands and sensor measurements being compromised. In contrast, we introduce a resiliency mechanism in this paper that can ensure safety for the overall system when all the actuator commands and sensor measurements are compromised. In addition, this approach does not require the implementation of any detection algorithm. We leverage communication redundancy in the number of pathways across the network to guarantee safety when up to a certain percentage of those pathways are compromised. The conditions under which safety is guaranteed are presented along with the resiliency mechanism itself, and our results are illustrated via simulation.

Cyber attacks are becoming increasingly frequent, sophisticated, and stealthy. This makes it harder for cyber defence teams to keep up, forcing them to automate their defence capabilities in order to improve their reactivity and efficiency. Therefore, we propose a fully automated cyber defence framework that no longer needs support from humans to detect and mitigate attacks within a complex infrastructure. We design our framework based on a real-world case - Locked Shields - the world's largest cyber defence exercise. In this exercise, teams have to defend their networked infrastructure against attacks, while maintaining operational services for their users. Our framework architecture connects various cyber sensors with network, device, application, and user actuators through an artificial intelligence (AI)-powered automated team in order to dynamically secure the cyber environment. To the best of our knowledge, our framework is the first attempt towards a fully automated cyber defence team that aims at protecting complex environments from sophisticated attacks.

We consider the problem of security for unmanned aerial vehicle flight control systems. To provide a concrete setting, we consider the security problem in the context of a helicopter which is compromised by a malicious agent that distorts elevation measurements to the control loop. This is a particular example of the problem of the security of stochastic control systems under erroneous observation measurements caused by malicious sensors within the system. In order to secure the control system, we consider dynamic watermarking, where a private random excitation signal is superimposed onto the control input of the flight control system. An attack detector at the actuator can then check if the reported sensor measurements are appropriately correlated with the private random excitation signal. This is done via two specific statistical tests whose violation signifies an attack. We apply dynamic watermarking technique to a 2-rotor-based 3-DOF helicopter control system test-bed. We demonstrate through both simulation and experimental results the performance of the attack detector on two attack models: a stealth attack, and a random bias injection attack.

As a leading industrial wireless standard, WirelessHART has been widely implemented to build wireless sensor-actuator networks (WSANs) in industrial facilities, such as oil refineries, chemical plants, and factories. For instance, 54,835 WSANs that implement the WirelessHART standard have been deployed globally by Emerson process management, a WirelessHART network supplier, to support process automation. While the existing research to improve industrial WSANs focuses mainly on enhancing network performance, the security aspects have not been given enough attention. We have identified a new threat to WirelessHART networks, namely smart selective jamming attacks, where the attacker first cracks the channel usage, routes, and parameter configuration of the victim network and then jams the transmissions of interest on their specific communication channels in their specific time slots, which makes the attacks energy efficient and hardly detectable. In this paper, we present this severe, stealthy threat by demonstrating the step-by-step attack process on a 50-node network that runs a publicly accessible WirelessHART implementation. Experimental results show that the smart selective jamming attacks significantly reduce the network reliability without triggering network updates.

In this paper, we investigate joint sensor-actuator cyber attacks in discrete event systems. We assume that attackers can attack some sensors and actuators at the same time by altering observations and control commands. Because of the nondeterminism in observation and control caused by cyber attacks, the behavior of the supervised systems becomes nondeterministic and deviates from the target. We define two bounds on languages, an upper-bound and a lower-bound, to describe the nondeterministic behavior. We then use the upper-bound language to investigate the safety supervisory control problem under cyber attacks. After introducing CA-controllability and CA-observability, we successfully solve the supervisory control problem under cyber attacks.

Cyber-physical systems are vulnerable to attacks that can cause them to reach undesirable states. This paper provides a theoretical solution for increasing the resiliency of control systems through the use of a high-authority supervisor that monitors and regulates control signals sent to the actuator. The supervisor aims to determine the control signal limits that provide maximum freedom of operation while protecting the system. For this work, a cyber attack is assumed to overwrite the signal to the actuator with Gaussian noise. This assumption permits the propagation of a state covariance matrix through time. Projecting the state covariance matrix on the state space reveals a confidence ellipse that approximates the reachable set. The standard deviation is found so that the confidence ellipse is tangential to the danger area in the state space. The process is applied to ship dynamics where an ellipse in the state space is transformed to an arc in the plane of motion. The technique is validated through the simulation of a ship traveling through a narrow channel while under the influence of a cyber attack.

In this research we have explained to set up an Infrared Array Sensor system that is IOT based in order to provide security at remote location. We have tried to Establishment of cloud environment to host IOT application & Development of IOT Application using Asp.net with C\# programming platform. We have Integrated IOT with Infrared Array sensors in order to implement proposed work. In this research camera captures the external event and sent signal to Infrared grid array sensor. Internet of Things (IoT) would enable applications of utmost societal value including smart cities, smart grids & smart healthcare. For majority of such applications, strict dependability requirements are placed on IOT performance, & sensor data as well as actuator commands must be delivered reliably & timely.

This paper presents a new micro linear actuator design. The North-South (NS) permanent magnet array configuration is assembled as the mobile part. The fixed part is designed to two-phase planar coils with no crossings avoiding interferences between overlapped conductors. The analytical calculation of the permanent magnet array verifies the feasibility of the finite element simulation. And then electromagnetic optimizations based on simulation to maximize the average thrust and minimize thrust ripple. In order to deal with millimeter level structure design, a microfabrication approach is adopted to process the new micro linear actuator in silicon material. The new micro linear actuator is able to perform millimeter level displacement strokes along a single axis in the horizontal plane. The experimental results demonstrate that the new micro linear actuator is capable of delivering variable strokes up to 5 mm with a precision error of 30 μm in position closed loop control and realizes the maximum velocity of 26.62mm/s with maximum error of 4.92%.

This research work consists in to design a system of occupancy simulation in smart homes based on IoT, in order to create configurations within a home that make look like the daily behavior of home inhabitants. Due to the high rate of burglary in uninhabited places, reaching an 9% in average in 2019 in the Chilean case, technologies have been involved with greater emphasis on improving security systems, where the implementation of the Internet of Things will allow rapid action against the intruder detection in those places. The proposed IoT system is based on a motion sensor, actuators as relays and lights, Arduino platform to control system, and a Amazon Echo virtual assistant to interface with inhabitants. The main contribution of this prototype security system is the integration of different IoT (Adafruit, IFTTT) and control platforms (Arduino uno and NodeMCU), virtual assistant (Alexa) and actuators, which has features that can be replicated in larger processes and with a larger number of devices. The results demonstrate that security system create an environment occupied by owners without to be inside home, through sensors and actuators.

The limited output of various drives means a challenge in controller design whenever the acceleration need of the "nominal trajectory to be tracked" temporarily exceeds the abilities of the saturated control system. The prevailing control design methods can tackle this problem either in a single theoretical step or in two consecutive steps. In this latter case in the first step the design happens without taking into account the actuator constraints, then apply a saturation compensator if the phenomenon of windup is observed. In the Fixed Point Iteration- based Adaptive Control (FPIAC) that has been developed as an alternative of the Lyapunov function-based approach the actuator saturation causes problems in its both elementary levels: in the kinematic/kinetic level where the desired acceleration is calculated, and in the iterative process that compensates the effects of modeling errors of the dynamic system under control and that of the external disturbances. The here presented approach tackles this problem in both levels by relatively simple considerations. To illustrate the method's efficiency simulation investigations were done in the FPIAC control of a modification of the van der Pol oscillator to which an additional strongly nonlinear term was added.

This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.

The progress made in terms of controller technologies with the introduction of remotely-accessibility capacity in the digital controllers has opened the door to new cybersecurity threats on the Industrial Control Systems (ICSs). Among them, some aim at damaging the ICS's physical system. In this paper, a corrupted controller emitting a non-legitimate Pulse Width Modulation control signal to an Electro-Mechanical Actuator (EMA) is considered. The attacker's capabilities for accelerating the EMA's aging by inducing Partial Discharges (PDs) are investigated. A simplified model is considered for highlighting the influence of the carrier frequency of the control signal over the amplitude and the repetition of the PDs involved in the EMA's aging.

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.