Attacks In Cyber-physical Systems Research Articles

Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep, and Exploratory Works

Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states, and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring a CPS’s physical state back to a target condition under adversarial attacks. To call for attention on CPS recovery and identify existing efforts, we have surveyed a total of 30 relevant papers. We identify a major partition of the proposed recovery strategies: shallow recovery vs. deep recovery, where the former does not use a dedicated recovery controller while the latter does. Additionally, we surveyed exploratory research on topics that facilitate recovery. From these publications, we discuss the current state-of-the-art of CPS recovery, with respect to applications, attack type, attack surfaces, and system dynamics. Then, we identify untouched sub-domains in this field and suggest possible future directions for researchers.

Identification and analysis of stochastic deception attacks on cyber–physical systems

Cyber–Physical Systems (CPSs) refer to control systems which are composed of sensors, actuators, computers and network components. These systems are vulnerable to unforeseen failures and external malicious attacks. In this paper, we analyze the stability of CPSs under stochastic deception attacks. To this end: (i) we propose a statistical Intrusion Detection System (IDS) for detection of deception attacks in CPSs; (ii) identify the place of such attacks by taking advantage of a novel cryptographic adversarial model; (iii) based on the real-time data and characterizing the deception attacks by IDS, we analyze the effect of both adaptive and blind/random types of deception attacks on the stability of CPSs. We do this through Markov chain modeling and subsequently extract the sufficient stability conditions. (iv) as an extra effort, introduce a new adaptive and intelligent deception attack. We validate our findings by illustrative examples at the end of the paper. Our results show that proposed IDS can detect deception attacks with low false positive and negative rates in real time. The results also confirm the validity of the theoretically-predicted stability conditions.

Stochastic Event-Based Physical Watermarks Against Replay Attacks in Cyber Physical Systems

Stochastic Event-Based Physical Watermarks Against Replay Attacks in Cyber Physical Systems

Probability-Based Stochastic Stealthy Attacks in Cyber-Physical Systems

Probability-Based Stochastic Stealthy Attacks in Cyber-Physical Systems

Emerging framework for attack detection in cyber-physical systems using heuristic-based optimization algorithm.

In recent days, cyber-physical systems (CPS) have become a new wave generation of human life, exploiting various smart and intelligent uses of automotive systems. In these systems, information is shared through networks, and data is collected from multiple sensor devices. This network has sophisticated control, wireless communication, and high-speed computation. These features are commonly available in CPS, allowing multi-users to access and share information through the network via remote access. Therefore, protecting resources and sensitive information in the network is essential. Many research works have been developed for detecting insecure networks and attacks in the network. This article introduces a framework, namely Deep Bagging Convolutional Neural Network with Heuristic Multiswarm Ant Colony Optimization (DCNN-HMACO), designed to enhance the secure transmission of information, improve efficiency, and provide convenience in Cyber-Physical Systems (CPS). The proposed framework aims to detect attacks in CPS effectively. Compared to existing methods, the DCNN-HMACO framework significantly improves attack detection rates and enhances overall system protection. While the accuracy rates of CNN and FCM are reported as 72.12% and 79.56% respectively, our proposed framework achieves a remarkable accuracy rate of 92.14%.

Tampering attack detection for remote interval observer

In cyber–physical systems (CPSs), secure estimation observes the states of a physical plant remotely with potentially attacked data. Most attacks in CPSs are carefully designed such that they can not only ruin the estimation but also bypass the detector. These attacked estimations would corrupt decision-making and control, leading to decreased production efficiency and even equipment damage. Therefore, detecting these attacks is of great importance. It is noted that one of the reasons that these attacks can be successfully launched is that attackers can inject arbitrary false data into the communication channel at any time step, which makes it possible that the transmission data is tampered with by any designed attack sequences. In this paper, a modified data transmission mechanism is deployed to limit the attacker’s ability, and attack detectors are designed for detecting tampering attacks. Specifically, first of all, a one-bit event-triggered mechanism is deployed to binarize the transmitted data. Secondly, a primary detector is deployed to detect non-binary data received by the observer, and hence the attacker’s ability is greatly reduced. Thirdly, a main detector is designed, which definitely detects tampering attacks that can bypass the primary detector.The necessary and sufficient conditions for definite attack detection are given, with which the gain of interval observable and the threshold of the event-trigger mechanism are designed. Finally, simulation results are presented to verify the analysis.

An Active Detection Strategy Based on Dimensionality Reduction for False Data Injection Attacks in Cyber-Physical Systems

In recent years, different intelligent and undetectable cyber-attacks against networked control systems (e.g., replay and covert attacks) have been investigated. In this paper, we design a novel control architecture that prevents their existence. In particular, first, we propose to encode the sensor outputs into a randomly changing lower-dimensional space obtained by means of principal component analysis. Such a transformation ensures optimal information reconstruction on the controller's side, and it prevents the attacker from accessing the original sensor measurements, nullifying the possibility of perfect stealthy attacks. Then, on the controller's side, we design a passive Gaussian anomaly detector that leverages the output of an unknown input observer ad-hoc designed to estimate the system's states and unknown inputs simultaneously. It is formally shown that the proposed detection strategy is able to discover the presence of replay and covert attacks. Simulation results obtained considering a quadruple water tank system confirm the capability of the developed architecture.

Detecting covert channel attacks on cyber‐physical systems

AbstractCyberattacks on cyber‐physical systems (CPS) have the potential to cause widespread disruption and affect the safety of millions of people. Machine learning can be an effective tool for detecting attacks on CPS, including the most stealthy types of attacks, known as covert channel attacks. In this study, the authors describe a novel hierarchical ensemble architecture for detecting covert channel attacks in CPS. Our proposed approach uses a combination of TCP payload entropy and network flows for feature engineering. Our approach achieves high detection performance, shortens the model training duration, and shows promise for effective detection of covert channel communications. This novel architecture closely mirrors the CPS attack stages in real‐life, providing flexibility and adaptability in detecting new types of attacks.

Detection and Performance Compensation for Linear $\epsilon$-Stealthy Attacks in Cyber-Physical Systems

In this paper, a precisely designed system framework for linear cyber-physical systems (CPSs) is presented to address the detection and performance compensation problems caused by linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -stealthy attacks. In particular, this paper constructs the security modules (data preprocess and data recovery) to assist the detectors (main detector and auxiliary detector) to alert the system anomalies. When no information is leaked, a parameter selection problem is analyzed to help the detector discover the attack. For the cases of model information leakage, we resort to the difference between the main detector and auxiliary detector to reveal the existence of linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -stealthy attacks. Under three different attack scenarios, this paper derives the corresponding attack-compensation mechanisms to ensure that the performance losses of systems are bounded. Finally, a three-tank system is provided to verify the effectiveness of the theoretical results.

Adaptive Security Control Against False Data Injection Attacks in Cyber-Physical Systems

In this paper, for a class of nonlinear cyber-physical systems (CPSs), the problem of adaptive security control for false data injection (FDI) attacks on the sensor and actuator is solved. Both sensor and actuator are destroyed by attackers, which makes the feedback control design unable to access the traditional error surface. Firstly, a state observer is constructed to mitigate the impact of sensor attacks. And then neural networks are used to approximate the nonlinear term and compensate for state-dependent actuator attacks. Finally, in order to reduce the impact of FDI attacks, we design a special time-varying symmetry barrier function in backstepping control design, which can achieve specific output signal constraints under FDI attacks. Through the above control strategy, the output constraint control problem of CPSs under FDI attacks is solved. Finally, a numerical simulation example demonstrates the effectiveness of the proposed controller.

Detection and Identification of Sparse Sensor Attacks in Cyber-Physical Systems With Side Information

This paper investigates the attack detection and identification problem for cyber physical systems under sparse sensor attacks. A novel candidate set construction algorithm, where only partial combinations of different channels are checked, is proposed to efficiently generate the candidate set containing all the possible sets of corrupted channels under stealthy attacks. Besides, through introducing a unified attack model which precisely characterizes the attack performance and stealthiness, necessary and sufficient conditions for the existence of undetectable/unidentifiable attacks are proposed. Based on the obtained conditions, a novel attack detection and identification strategy is proposed. Compared with the existing methods focusing only on the measurements, more attacks can be detected/identified through introducing side information and the reliability of detector is enhanced through making full use of the attack performance analysis result. Finally, a numerical simulation is provided to illustrate the correctness and effectiveness of the proposed conditions and methods.

Optimal strictly stealthy attacks in cyber-physical systems with multiple channels under the energy constraint

This work considers an innovation-based attack policy in cyber-physical systems with multiple channels. The linear deception attack policy only allows the partial channels to be compromised at each step due to the energy constraint in multiple channels. Compared to the existing results with the single channel, the attack matrices and the positions are time-varying in this work. For the multi-channel systems under the energy constraint, the attack policy is designed to determine the optimal attack locations to degrade the estimation performance of the system to the most extent. Then, it is proved that achieving the optimal attack policy is equivalent to solving a limited optimisation problem and a 0–1 programming problem, respectively. Furthermore, simulated examples are presented to illustrate the developed results.

Mitigating Adversarial Attacks on Data-Driven Invariant Checkers for Cyber-Physical Systems

The use of <i>invariants</i> in developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems&#x00A0;(CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system&#x0027;s requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While data-driven invariant generation can be fully automated, design-driven invariant generation has a substantial manual intervention. In this paper, we aim to highlight the shortcomings in data-driven invariants by demonstrating a set of adversarial attacks on such invariants. We propose a solution strategy to detect such attacks by complementing them with design-driven invariants. We perform all our experiments on a real water treatment testbed. We shall demonstrate that our approach can significantly reduce false positives and achieve high accuracy in attack detection on CPSs.

Runtime System Support for CPS Software Rejuvenation

Software rejuvenation, which was originally introduced to deal with performance degradation due to software aging, has recently been proposed as a mechanism to provide protection against run-time cyber attacks in cyber-physical systems (CPSs). Experiments have demonstrated that CPSs can be protected from attacks that corrupt run-time code and data by periodically restoring the run-time system with an uncorrupted image. Control theoretic and empirical methods have been developed to determine the timing and mode-switching conditions for CPS software rejuvenation (CPS SR) that will guarantee system safety. This paper presents the requirements that need to be met by the run-time system to support CPS SR. It also presents an implementation and demonstration of the run-time system for the PX4 autopilot system for autonomous vehicles.

Correction: Real-time detection of deception attacks in cyber-physical systems

Correction: Real-time detection of deception attacks in cyber-physical systems

Real-time detection of deception attacks in cyber-physical systems

AbstractDetection of deception attacks is pivotal to ensure the safe and reliable operation of cyber-physical systems (CPS). Detection of such attacks needs to consider time-series sequences and is very challenging especially for autonomous vehicles that rely on high-dimensional observations from camera sensors. The paper presents an approach to detect deception attacks in real-time utilizing sensor observations, with a special focus on high-dimensional observations. The approach is based on inductive conformal anomaly detection (ICAD) and utilizes a novel generative model which consists of a variational autoencoder (VAE) and a recurrent neural network (RNN) that is used to learn both spatial and temporal features of the normal dynamic behavior of the system. The model can be used to predict the observations for multiple time steps, and the predictions are then compared with actual observations to efficiently quantify the nonconformity of a sequence under attack relative to the expected normal behavior, thereby enabling real-time detection of attacks using high-dimensional sequential data. We evaluate the approach empirically using two simulation case studies of an advanced emergency braking system and an autonomous car racing example, as well as a real-world secure water treatment dataset. The experiments show that the proposed method outperforms other detection methods, and in most experiments, both false positive and false negative rates are less than 10%. Furthermore, execution times measured on both powerful cloud machines and embedded devices are relatively short, thereby enabling real-time detection.

Window-type detector for stealthy false data injection attack in cyber-physical systems

In recent years, the security issues of cyber-physical systems (CPSs) have attracted extensive attention from researchers. In this paper, a novel window-type detector (WTD) is designed for stealthy false data injection (FDI) attack targeted at CPSs. In the absence of attack, the evaluation value of a WTD is distributed because of the existence of Gaussian noise. In the presence of attack, the evaluation value of the WTD is the result of accumulating the residual data caused by attack signals, which is similar to the mechanism of a summation (SUM) detector. It is proved that when the duration of system operation is much larger than the attack duration squared, evaluation value of an SUM detector is distributed due to the huge number of tolerable historical data. And the structure of finite window size prevents the WTD from failure in such a situation, which equips it with better performance in the detection of attacks with medium duration. Furthermore, it is proved that the closer the window size is to the attack duration, the larger the evaluation value is, which means that the better detection performance could be achieved. Finally, numerical examples are given to verify the characteristics of the WTD.

Robust and Resilient Distributed MPC for Cyber-Physical Systems Against DoS Attacks

In this paper, considering the ubiquitously existing cyber attacks in cyber-physical systems (CPSs), we present a robust and resilient distributed model predictive control (MPC) strategy for CPSs with multi-agent architecture under denial-of-service (DoS) attacks to achieve the goal of cooperative regulation with all agents' states being regulated to their equilibrium. Each agent in the CPSs is subject to external disturbances, and the communication channels among agents might be affected by randomly occurring DoS attacks. To tackle these issues, firstly, a novel robustness constraint is designed to handle the uncertainties in the MPC algorithm. By adding this constraint, the state of the nominal system can be confined in a shrinking and tighter range compared to the classical MPC approach, thus resulting in enhanced robustness against uncertainties. Furthermore, a lengthened sequence transmission strategy is proposed to mitigate the effect of the lack of information in the communication channels induced by DoS attacks. At each time instant, the controller of each agent utilizes the predicted state information to compensate for the transmission block-out from one agent to another. Moreover, recursive feasibility for the control framework and the closed-loop stability for the overall system are guaranteed by theoretical analysis. Finally, simulation and comparison studies demonstrate the effectiveness of the proposed robust and resilient distributed MPC strategy.

Sensor Threat Isolation for Cyber-Physical Systems

This paper considers the isolability of sensor faults and malicious cyber attacks in cyber-physical systems. Attacks and faults are characterized by their basis functions, which are utilized for threat isolation and to characterize their isolability. An isolability metric is proposed that exploits the threat basis function to quantify the threat isolability. It is rigorously proved that a threat is non-isolable if and only if this metric is zero. A methodology to analytically calculate the proposed isolability metric is developed by solving a minimax optimization problem. Our findings are validated with numerical simulations.

Supremal and Robust Control against Actuator Jamming Attacks in Cyber-Physical Systems

Supremal and Robust Control against Actuator Jamming Attacks in Cyber-Physical Systems