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In this paper, we consider the resilient distributed estimation in adversarial networks, compromised by false data injection (FDI) attacks. We develop a two-step distributed approach to detect the compromised nodes of the network due to FDI attacks. Specifically, we develop a distributed correntropy-based discrimination scheme followed by a distributed state perception scheme to distinguish the compromised nodes from the secure, such that the reliable neighbors could be determined by each node in a distributed manner. We further theoretically analyze the mean and mean-square performance of the resilient distributed estimation, and propose an optimal reference neighbor selection scheme to enhance the network estimation performance. Illustrative simulations validate the superior performance of both FDI attack detection and parameter estimation of the proposed distributed algorithms for adversarial networks, especially under heavy and time-varying FDI attacks. The theoretical results well predict the performance of the proposed resilient distributed estimation algorithm. [ABSTRACT FROM AUTHOR]

Recent researches show that deep learning model is susceptible to backdoor attacks. Many defenses against backdoor attacks have been proposed. However, existing defense works require high computational overhead or backdoor attack information such as the trigger size. In this paper, we propose a novel backdoor detection method based on intentional adversarial perturbations. The proposed method leverages intentional adversarial perturbation to detect whether an image contains a trigger, which can be applied in both the training stage and the inference stage (sanitize the training set in training stage or detect the backdoor instances in inference stage). Specifically, given an untrusted image, the adversarial perturbation is added to the image intentionally. If the prediction of the model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance. Compared with most existing defense works, the proposed method is faster and introduces less computational overhead during backdoor detection process. Moreover, the proposed method maintains the visual quality of the image (as the ℓ 2 norm of the added perturbation is as low as 2.8715, 3.0513 and 2.4362 on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively). Experimental results show that, for general backdoor attack, the backdoor detection rate of the proposed defense method is 99.63%, 99.76% and 99.91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively. For invisible backdoor attack, the backdoor detection rate of the proposed defense method is 99.75% against blended backdoor attack and 98.00% against sample-specific backdoor attack. It is also demonstrated that the proposed method can achieve high defense performance against backdoor attacks under different attack settings (trigger transparency, trigger size and trigger pattern). In addition, the experimental comparison with related work demonstrates that the proposed method has better detection performance and higher detection efficiency. [ABSTRACT FROM AUTHOR]

Forgery of a signature with the aim of deception is a serious crime. Machine learning is often employed to detect real and forged signatures. In this study, we present results which argue that robotic arms and generative models can overcome these systems and mount false-acceptance attacks. Convolutional neural networks and data augmentation strategies are tuned, producing a model of 87.12% accuracy for the verification of 2,640 human signatures. Two approaches are used to successfully attack the model with false-acceptance of forgeries. Robotic arms (Line-us and iDraw) physically copy real signatures on paper, and a conditional Generative Adversarial Network (GAN) is trained to generate signatures based on the binary class of 'genuine' and 'forged'. The 87.12% error margin is overcome by all approaches; prevalence of successful attacks is 32% for iDraw 2.0, 24% for Line-us, and 40% for the GAN. Fine-tuning with examples show that false-acceptance is preventable. We find attack success reduced by 24% for iDraw, 12% for Line-us, and 36% for the GAN. Results show exclusive behaviours between human and robotic forgers, suggesting training wholly on human forgeries can be attacked by robots, thus we argue in favour of fine-tuning systems with robotic forgeries to reduce their prevalence. • Development of a computer vision-based system for signature spoofing attack detection. • A Conditional GAN can generate "real" and "fake" signatures. • Two robots can physically replicate human signatures with pen and paper. • The GAN and both robots can fool the model and mount false-acceptance attacks. • Verification model can be defended by fine-tuning on generative and robotic forgeries. [ABSTRACT FROM AUTHOR]

• Propose an IoMT security defense method based on blockchain and fuzzy set theory. • Propose DFCM algorithm to solve the clustering problem of balanced data. • Use FSVM model to classify data points far from the sample center. • Use the combination of DFCM and FSVM in IoT attack detection. This work aims to improve the stability and efficiency of the Internet of Medical Things (IoMT), strengthen the security and privacy of personal information, and create a healthy and safe network environment. First, art therapy in the current medical environment is discussed, and the security defenses of the IoMT supporting art therapy are studied. Then, a security defense method for IoMT is proposed by combining basic Blockchain knowledge with the Fuzzy Sets Theory. Specifically, a Density-awarded Fuzzy C-means (DFCM) algorithm is developed to solve the clustering issue of unbalanced data sets. Besides, the Fuzzy Support Vector Machine (FSVM) model is used to classify data points far from the sample center to increase classification accuracy. In the simulation experiment, the DFCM algorithm achieves a pleasing clustering effect on the four data sets, involving the data sets with density changes and imbalanced sample points within the cluster. Moreover, the combination of DFCM and FSVM (DFCM-FSVM) for attack detection in the Internet of Things (IoT) realizes the optimal accuracy in the detection of different behaviors compared with similar algorithms; the detection accuracy of normal behavior, scheduled attack, gray hole attack, and black hole attack is 99.8%, 89.8%, 99.4%, and 91.5%, respectively. This work creatively proposes a hybrid intrusion detection method based on fuzzy theory. As the second line of defense in security protection, it plays a critical role and is suitable for the network environment of the perception layer. In conclusion, the attack detection method based on Blockchain combined with the Fuzzy Sets Theory can precisely identify potential attacks in abnormal data, which has important practical value for maintaining data security and the regular operation of the IoMT. [ABSTRACT FROM AUTHOR]

This paper studies the distributed adaptive fuzzy fault-tolerant control for multi-agent systems (MASs) with node faults and denial-of-service (DoS) attacks. On account of DoS attacks, the fixed network topology is changed into switching topologies, which brings about many difficulties in designing fault-tolerant controllers. In addition, node faults and nonlinearity terms make it more challenging to analyze the stability of MASs with DoS attacks. To cope with the nonlinear terms in MASs, a fuzzy logic systems method is provided. Also, based on the switching mechanism, the average dwell time technique, and the Lyapunov stability theory, a distributed adaptive fuzzy fault-tolerant controller is proposed, and the bounded synchronization of the MASs is achieved. A simulation example is used to validate the efficiency of the proposed approach. • Instead of considering node faults and malicious attacks separately, an adaptive fuzzy FTC strategy is constructed for nonlinear MASs with node faults and DoS attacks. • The fuzzy logic systems (FLSs) approach is utilized to remove the constraint that the nonlinear dynamics of the leader and follower are required to match each other. • Based on the Lyapunov stability analysis, an average dwell time (ADT) condition is provided to guarantee the bounded synchronization of MASs. [ABSTRACT FROM AUTHOR]

This paper studies the bipartite secure control design problem for nonlinear multi-agent systems (MASs) subject to denial-of-service (DoS) attacks over a signed digraph. By proposing an anti-attack control method, the nonlinear MASs can achieve the bipartite secure control goal in an insecure network and physical environment. In addition, to save communication resources, a novel distributed adaptive self-triggered control (ASTC) mechanism is proposed. Different from the traditional self-triggered control, the trigger interval can be dynamically adjusted according to the situation that the followers converge to the convex hull formed by the leaders, which makes the proposed control protocol based on ASTC able to balance the system performance and communication resources under DoS attacks through scheduling the inherent system resources. Furthermore, in order to overcome the problem of complexity explosion, a command filter is introduced into the design process to simplify the design process. The validity of our control scheme is demonstrated through a simulation example. [ABSTRACT FROM AUTHOR]

This article addresses the observer-based event-triggered non-parallel distribution compensation (PDC) H ∞ control issue for networked T-S fuzzy systems (TSFSs) under actuator failures and aperiodic denial-of-service (DoS) attacks. In order to alleviate the interference of network transmission, a fuzzy observer following the asynchronous premise variables as the system is constructed, and we design an observer-based switched fuzzy controller through a non-PDC scheme. And an event-triggered scheme (ETS) is designed to save the limited network bandwidth. Then, based on switching strategy and quantifying the DoS attacks signal indexes, the networked TSFSs can be converted into a new switched augmented system. Moreover, in virtue of piecewise Lyapunov-Krasovskii functional (LKF) method, we propose sufficient conditions to guarantee that the networked TSFS is exponential stability (ES) with a desired H ∞ performance. Further, a collaborative design project is presented to obtain triggering parameters, observer gains, and controller gains, and we derive the quantitative relationship between sampling period, triggering parameters, system parameters, actuator fault matrix, and DoS attacks signal indexes. At last, two practical examples are employed to certify the effectiveness and superiority of the presented techniques. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the distributed resilient output-feedback containment problem of linear multi-agent systems (MASs) with a dynamic Luenberger observer under composite attacks, including denial of service (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks (CAs), and actuation attacks (AAs). By using the digital twin framework, a twin layer (TL) is introduced for the cyber-physical layer (CPL), which decomposes the problem of the composite attack into a DoS attack on the TL and an unknown AAs on the CPL for analysis and countermeasures. A sampled-based event-triggered control protocol is employed to save the communication resources of MASs. To achieve the goal of containment control under the DoS attack, a backup topology is employed to repair the communication channels that have been compromised by the DoS attacks. Then, a new adaptive controller is proposed to synchronize the MASs with a polluted communication network caused by unbounded AAs. This controller can eliminate the chattering phenomenon of the system and enable the containment error among the agents to reach a uniformly ultimately bounded (UUB) convergence. Finally, the practicality and effectiveness of the theoretical results are verified through simulations. [ABSTRACT FROM AUTHOR]

In this paper, an outlier-resistant sequential fusion problem is concerned for cyber-physical systems with quantized measurements under denial-of-service attacks. The multi-sensor measurements are quantized by a bank of logarithmic quantizers before entering into communication networks. The denial-of-service attack is, from the defenders' perspective, regarded to be randomly occurring and such an occurrence is governed by a Bernoulli-distributed sequence of certain probability distribution. To suppress effects from measurement outliers onto innovations, tailored saturation functions are dedicatedly introduced to filter structures at both local and fusion stages, thereby keeping satisfactory fusion performance. By finding solutions to a set of matrix difference equations, upper bounds are initially acquired on estimator error covariances, and associated estimator parameters are subsequently secured via minimizing these acquired bounds. Two examples are finally presented to showcase the applicability of this outlier-resistant sequential fusion algorithm. • An outlier-resistant sequential fusion problem is concerned. • Cyber-physical systems with quantized measurements are investigated. • Upper bounds are initially acquired on estimator error covariances. • Associated estimator parameters are subsequently secured. • A sequential fusion algorithm is developed to ensure the applicability. [ABSTRACT FROM AUTHOR]

• A class of more general link attacks with multiple patterns is considered. • A SETCM is proposed to reduce coupling communication burden and save coupling resources. • The security synchronization criteria under link attacks with multiple patterns are derived. This paper concentrates on the secure synchronization problem against link attacks in complex networks with event-triggered coupling. The link attacks for each link are independent, which means that there are multiple attack patterns for coupling communication topology. Then, the relevant model of link attacks with multiple patterns is constructed to analyze the effect on synchronization error system. Furthermore, to reduce communication burden and save coupling resources, a sampled-data-based event-triggered coupling mechanism is established. In this situation, the triggering condition is supervised only at discrete sampling instants. By employing Lyapunov theory and some inequality techniques, sufficient conditions are given to guarantee the synchronization against link attacks with multiple patterns. At last, a simulation example on Chua's circuit network is provided to demonstrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Nowadays, cybersecurity challenges and their ever-growing complexity are the main concerns for various information technology-driven organizations and companies. Although several intrusion detection systems have been introduced in an attempt to deal with zero-day cybersecurity attacks, computer systems are still highly vulnerable to various types of distributed denial of service (DDoS) attacks. This complicated cyber-attack caused many system failures and service disruptions, resulting in billions of dollars of financial loss and irrecoverable reputation damage in recent years. Considering the nonnegligible importance of business continuity in the Industry 4.0 era, this paper presents a comprehensive, systematic survey of DDoS attacks. It also proposes a hierarchy for this severe cyber threat, besides conducting deep comparisons from various perspectives between the studies published by reputed venues in this area. Furthermore, this paper recommends the most effective defensive strategies, with a focus on recently offered fuzzy-based detection methods, to mitigate such threats and bridge the gaps existing in the current intrusion detection systems and related works. The outcomes and key findings of this survey paper are highly advantageous for private companies, enterprises, and government agencies to be implemented in their local or global businesses to significantly improve business sustainability. [ABSTRACT FROM AUTHOR]

• An event-based containment controller is innovatively designed in MASs. • The network injected deception attack is considered through the impulsive theory. • The failures occurring inside the actuators are considered to enhance robustness. This paper studies a containment control problem of multi-agent systems. A two-layer distributed control framework is proposed to handle the external network attack and internal actuator failure. In the network layer, an event-triggered impulsive control protocol is designed to handle the deception attack, which can deal with stochastic attacks injected into the controller under limited energy bound. Compared with the traditional event-triggered-based continuous control method, the impulsive control mechanism can further reduce energy consumption with better robustness. In the physical layer, a fault-tolerant controller, which considers multiplicative failure, is investigated to maintain the system's stability. Rigorous proofs for controller stability are given through Lyapunov theory, and Zeno behavior is also avoided within the designed control mechanism. Finally, a numerical simulation is demonstrated to test and verify the proposed control scheme. [ABSTRACT FROM AUTHOR]

• An event-based containment controller is innovatively designed in MASs. • The network injected deception attack is considered through the impulsive theory. • The failures occurring inside the actuators are considered to enhance robustness. This paper studies a containment control problem of multi-agent systems. A two-layer distributed control framework is proposed to handle the external network attack and internal actuator failure. In the network layer, an event-triggered impulsive control protocol is designed to handle the deception attack, which can deal with stochastic attacks injected into the controller under limited energy bound. Compared with the traditional event-triggered-based continuous control method, the impulsive control mechanism can further reduce energy consumption with better robustness. In the physical layer, a fault-tolerant controller, which considers multiplicative failure, is investigated to maintain the system's stability. Rigorous proofs for controller stability are given through Lyapunov theory, and Zeno behavior is also avoided within the designed control mechanism. Finally, a numerical simulation is demonstrated to test and verify the proposed control scheme. [ABSTRACT FROM AUTHOR]

• We develop a mathematical framework that allows for the definition of interval principal component analysis. • Interval-valued principal components are defined as linear combinations of the original interval-valued variables of maximum symbolic variance. • The projection of the original symbolic observations on the reduced space spanned by the first principal components allows for the use of interval principal component analysis as a dimensionality reduction method. • We develop an outlier-detection method based on the first interval-valued principal components. • We explore real world data from the telecommunications sector, to detect Internet redirection attacks. Principal Component Analysis is a well-known method that can be used for dimensionality reduction, a useful technique in the Big Data era. There have been a series of proposed adaptations of the Principal Component Analysis method for interval-valued symbolic data, all of which have the downside of having intermediate steps that deal with conventional data. In this work, we put forward an interval Principal Component Analysis that only deals with symbolic data by developing a theoretical framework that allows for the definition of symbolic principal components. This framework provides the mathematical tools needed to use the symbolic principal components to transform the original data in a way that is mathematically coherent with the remainder of the framework and defines the principal components as solutions to maximisation problems, similarly to what is done in conventional Principal Component Analysis. After the theoretical foundations are laid down, we explore real-world data from the telecommunications sector, in an attempt to detect Internet redirection attacks in real-time. In particular, we use our symbolic method to improve and simplify an anomaly detection method that has been proposed in the literature for conventional data. [ABSTRACT FROM AUTHOR]

This paper studies the problem of fractional-order impulsive security control for uncertain fractional-order delayed multi-agent systems (FDMASs) under Denial-of-Service (DoS) attack. New sufficient conditions to achieve impulsive secure consensus are analyzed. To determine the stability of the resulting error system, we utilized fractional-calculus theory, algebraic graph theory, Lyapunov functional. The influence of the impulsive control scheme depends on the order of the Caputo fractional-order systems addressed. It is shown that the agents can achieve an exponential consensus under the proposed impulsive control scheme. Finally, the effectiveness of the theoretical results is demonstrated by numerical examples and simulation results. [ABSTRACT FROM AUTHOR]

This paper studies model predictive security control (MPSC) for networked switched systems under denial-of-service (DoS) attacks. Most of existing works only adjust the triggering scheme when being attacked. Different from them, this paper proposes a novel timing-response event-triggering scheme (TR-ETS) to reduce the impact of attacks on system performance, which can not only configure system resources adaptively, but also accurately detect attack information and compensate the attacked data. Specifically, the proposed scheme includes two event-based triggers, which can dynamically and jointly regulate the communication/calculation ability, generate virtual attack sequences and acquire the number of passive packet loss. Then, based on the triggered states, a class of model predictive controllers is designed to optimize the control action. Due to possible strong attacks, a security control framework including network and local loops be introduced and a permissable type-switching mechanism (PTM) is used. Under the permissable controllers (i.e., network and local controllers), sufficient conditions for the stability of closed-loop switched systems are derived. In addition, a set of model predictive optimization algorithm using linear matrix inequalities (LMIs) technique is addressed. Finally, the effectiveness of the proposed method is verified by illustrative examples. [ABSTRACT FROM AUTHOR]

A poisoning attack method manipulating the training of a model is easily to be detected since the general performance of a model is downgraded. Although a backdoor attack only misleads the decisions on the samples with a trigger but not any samples, the strong association between the trigger and the class ID exposes the attack. The weak concealment limits the damage of current poisoning attacks to machine learning models. This study proposes a poisoning attack against deep neural networks, aiming to not only reduce the robustness of a model against adversarial samples but also explicitly increase its concealment, defined as the accuracy of the contaminated model on untainted samples. In order to improve the efficiency of poisoning sample generation, we propose training interval, gradient truncation, and parallel process mechanisms. As a result, the model trained on the poisoning samples generated by our method is easily misled by slight crafting, and the attack is difficult to be detected since the contaminated model performs well on clean samples. The experimental results show that our method significantly increases the attack success rate without a substantial drop in classification accuracy on clean samples. The transferability and instability of our model are confirmed experimentally. [ABSTRACT FROM AUTHOR]

Deep neural networks have superior transferability between different tasks. However, attackers may inject a backdoor into the pre-trained network to indirectly inject the backdoor into the user's new network which is transferred from the pre-trained network. The previous backdoor attack schemes cannot ensure that the backdoor in the pre-trained network will not be damaged during network transfer. To deal with this issue, we propose a universal robust backdoor attack scheme to injecting the backdoor into the feature maps of the pre-trained network. First, we show that abnormal values have favorable consistency and conductivity in the neural network. By using these properties, attackers can fine-tune the pre-trained network to create a path that can cause the feature maps of backdoor examples at the target layer to exhibit abnormal values. The backdoor injected with our scheme is robust and can cope with the following three challenging network transfer types: (a) the user fine-tunes all the layers. (b) the user retrains some layers of the network from scratch (c) the user adds the new layers for other categories of tasks. Finally, we experimentally verify that our scheme has a favorable attack success rate and is more robust than the state-of-the-art. [ABSTRACT FROM AUTHOR]

This article studies a dynamic event-triggered security control problem for networked control systems subject to deception attacks and packet dropouts. First, a combined cyber-attack model is proposed, which utilises two sets of independent stochastic sequences to reflect randomly occurring cyber-attacks. Subsequently, a dynamic event-triggered protocol is constructed to relieve the restricted bandwidth pressure by reducing the data transmission of the communication channel from the plant to the controller. With the consideration of randomly occurring deception attacks, packet dropouts, and dynamic event-triggered protocols, an online model predictive control algorithm is established to ensure the stochastic stability of the closed-loop model with expected H 2 / H ∞ performance. Finally, two examples are simulated to interpret the validity and effectiveness of the proposed design strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the security tracking controller design for discrete-time networked control systems (NCSs) with stochastic cyber-attacks via dynamic event-triggered communication approach (DETCA). The DETCA is introduced to adjust the amount of data transmission in the network based on variation of the tracking error while keeping the tracking performance of the system. Stochastic cyber-attacks such as the denial-of-service and deception attacks are assumed to be encountered during the signal transmitted in the network. A sufficient condition to guarantee the asymptotical stability of the tracking error system is achieved with the Lyapunov stability theory. The tracking controller gain and the event-triggering parameter are co-designed by solving a linear matrix inequality. Finally, two simulation examples are given to verify the availability of theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents a distributed consensus control algorithm for nonlinear multi-agent systems (MASs) with external disturbances and denial-of-service (DoS) attacks. Modified dynamic linearization is proposed for the linear transformation of the MASs, and an extended discrete state observer (EDSO) improves their robustness. An attack compensation scheme eliminates the effects of DoS attacks. Based on the EDSO and the attack compensation scheme, a distributed model-free adaptive control (DMFAC) algorithm is developed to achieve consensus control in a boundary, and simulations verify its superiority in tracking time and tracking accuracy over other control algorithms. [ABSTRACT FROM AUTHOR]

This article addresses the event-triggered control problem for networked control systems with uncertainties and denial-of-service (DoS) attacks. A resilient model-based hybrid dynamic event-triggered mechanism (HDETM) is proposed to defend against DoS attacks and reduce communication in sensor-controller-actuator networks. Owing to the introduction of regularization variables, Zeno behavior is naturally excluded, even if there exist disturbances. By using model-based computing, the novel HDETM outperforms the existing event-triggered mechanisms in terms of network utilization and robustness. To model and analyze the proposed HDETM, we propose a novel framework based on hybrid systems, which is different from the traditional discrete-time system framework. Meanwhile, the exponential stability and the L 2 -gain performance of the closed-loop system are guaranteed by utilizing the stability theorems for hybrid systems. In addition to the centralized model-based HDETM, a decentralized HDETM is also provided for large-scale systems which are composed of multiple interconnected subsystems. Finally, two examples are given that verify the improvement of the proposed model-based HDETM. [ABSTRACT FROM AUTHOR]

• We propose two algorithms to optimize adversarial perturbation and patch. • We discover that CAEs can quickly decrease the classification accuracy. • We find that two weak attack patterns can be combined to perform stronger attack. • We demonstrate the effectiveness and robustness of the proposed method. Although deep learning has made great progress in many fields, they are still vulnerable to adversarial examples. Many methods for generating adversarial examples have been proposed, which either contain adversarial perturbation or patch. In this paper, we explore the method that creates compound adversarial examples including both perturbation and patch. We show that fusing two weak attack modes can produce more powerful adversarial examples, where the patch covers only 1 % of the pixels at random location in the image, and the perturbation changes only by 2/255 in the original pixel value (scale to 0–1). For both targeted attack and untargeted attack, compound attack can improve the generative efficiency of adversarial examples, and can attain higher attack success rate with fewer iteration steps. The compound adversarial examples successfully attack the models with defensive mechanisms that previously can defend perturbation attack or patch attack. Furthermore, the compound adversarial examples show good transferability on normal trained classifiers and adversarial trained classifiers. Experimental results on a series of widely used classifiers and defense models show that the proposed compound adversarial examples have strong robustness, high effectiveness, and good transferability. [ABSTRACT FROM AUTHOR]

This paper mainly studies an event-based secure control problem for cyber-physical systems against false data injection (FDI) attacks. First, a dynamic event-triggered transmission protocol is established to schedule the transmitted data from the sensor to the controller. Subsequently, an imperfect FDI attack model is constructed considering the real network environment, which would bypass the traditional residual-based detection methods. To overcome this drawback, an attack detection strategy based on secure history information is designed, which effectively mitigates the negative impact of uncertainty on the detection results. Based on the detection results, an event-based secure linear quadratic Gaussian control scheme is designed, which can guarantee system performance and reduce the communication load effectively. Finally, a simulation experiment verifies that the proposed control method outperforms other algorithms in the literature, and the detection accuracy is increased by 91% to 98%. [ABSTRACT FROM AUTHOR]

Intelligent network systems have become an integral part of society, communication methods and corresponding protocols are the basis for the realization of information exchange in these systems. As network attacks often take advantage of the vulnerabilities in the protocols to launch attacks with different characteristics, many detection methods have been investigated by professionals. To address this challenge, we propose an approach that uses an algorithm for clustering to compute anomaly scores to initialize the classifier's sample weights and adaptively optimize these scores, which is then achieved by learning the adaptive weights for each group to achieve a group-level balance between different protocol attack groups. Our approach focuses on error-prone samples and aims to improve the adequate representation of a small number of protocol groups. At the same time, we reduce the inter-group difference and improve the accuracy of the classifier for better fairness. We construct closed-form solutions with adaptive weight assignment and use convex optimization to ensure theoretical convergence. To validate the theoretical superiority and accuracy of the methods, we conduct experiments on the KDD, NSLKDD, and CIC-IDS datasets and compared them to current state-of-the-art methods. Compared to the baseline method, our approach demonstrates improvements in both fairness indicators and accuracy across three datasets. These experimental results show that our proposed method outperforms existing methods in terms of performance. In addition, our technique exhibits robustness to noisy labels in various benchmark datasets. To enhance the reproducibility of the paper, we have uploaded the source code to GitHub at the following URL: https://github.com/dazhi-ui/Fairness_Detection. [ABSTRACT FROM AUTHOR]

This article addresses the secure consensus problem of fractional-order multiagent systems (FOMASs) under denial-of-service (DoS) and deception attacks in the intermittent impulsive control framework. Unlike previous cyber attack models, the multiple attacks are considered, which the agent-to-agent communication channels and the controller-to-actuator channels are randomly subjected to deception and DoS attacks, respectively. Some random variables that follow the Bernoulli distribution are proposed, which are related to the communication channels between adjacent agents to describe deceptive attack behavior. To reduce control costs and resist cyber attacks, a novel intermittent impulsive control strategy is designed to make the FOMASs achieve consensus faster, when DoS attacks are sleeping. The designed intermittent impulsive control includes the advantages of discrete sampling control and intermittent control, which means that it can reduce the injection of erroneous data by constraining the frequency of information transmission. Furthermore, some sufficient criteria including the parameters of multiple attacks are yielded to ensure mean-square quasi-consensus of FOMASs. Finally, two examples are provided to validate the rationality for the presented security control method in this study from the perspective of numerical simulation experiments. • A more general model of systems with multiple cyber attacks is considered. • A new secure intermittent impulsive consensus control strategy is designed. • Consensus criteria relying the frequency and duration of cyber attacks are proposed. [ABSTRACT FROM AUTHOR]

Large language models have revolutionized natural language processing, leading to the emergence of Embedding as a Service (EaaS). While EaaS facilitates access to advanced embedding models, it also presents challenges in copyright protection. Current research primarily relies on single-target watermarking frameworks, where a predefined vector is integrated as a watermark into text embeddings. However, these approaches are vulnerable to watermark information leakage. To investigate this issue, we introduce the Embedding Similarity Shift Attack (ESSA), an innovative attack algorithm designed to detect trigger instances in single-target watermarking systems by analyzing similarity shifts among constructed reference sentence pairs. Additionally, to defend against such an attack, we propose Adaptive Multi-Target Watermarking (AMT-WM). AMT-WM stands as the pioneering multi-target watermarking method aimed at safeguarding the copyright of EaaS. Specifically, AMT-WM constructs multiple watermarks through the utilization of orthogonal vectors to mitigate selection bias towards a particular vector. Furthermore, it incorporates a randomly selected sentence embedding as the base embedding to enhance the confidentiality of backdoored embeddings. For multi-target watermarking, we implement adaptive watermark injection and validation based on similarity. Comprehensive experiments conducted on various datasets validate the effectiveness of ESSA in trigger detection performance and the efficacy of AMT-WM in copyright protection. Our code will be available soon. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of data-driven sensor injection attack for cyber-physical systems (CPSs) under channel constraints, where the attacker can only access a part of the sensor channels and the accessible channels switch over time. To enhance the attack's effectiveness while maintaining its stealthiness, the design problem is formulated as a constraint-type L 2 -gain optimization problem. Then, by optimizing the attack-direction matrix using the data-driven parametrization method, an attack policy with channel constraints is proposed. Specifically, the necessary and sufficient design conditions are established in terms of the attack stealthiness. Furthermore, the optimized attack stealthiness index and attack effectiveness index under channel constrains are obtained, and it is theoretically proven that the attack performance is reduced due to the passive channel switching. The effectiveness of the data-driven attack policy is illustrated by the IEEE 6 bus power system. [ABSTRACT FROM AUTHOR]

The generation of optimal defense strategies in dynamic adversarial environments is crucial for cybersecurity. Recently, defense approaches based on evolutionary game theory have gained significant achievements. However, they would fail when facing complex networks and sophisticated attack strategies, due to the fatal drawbacks of defense strategy generation considering atomic attacks only. To relieve this issue, a generic approach for generating defense strategies using evolutionary game theory is proposed in this paper. Initially, a novel payoff quantification method for network attack-defense games based on attack graphs is designed. Innovatively, two factors concerning the decision-maker's degree of irrationality (DI) and the level of environmental security (LES) are introduced into the replicator dynamics equation to model the impacts on equilibrium solutions. Noting that Active Directory (AD) domain service is one of the most used and representative information security management system in Windows domains, from which attack graphs and paths can be plainly extracted and analyzed. Therefore, it is necessary and imperative to anchor AD to unfold the theoretical analyses and experiments validation based on a real environment. Case studies on a real-world AD network demonstrate that the proposed approach is effective and can generate stable and efficient defense strategies. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have substantially boosted the performance of monocular depth estimation (MDE), an essential component in fully-vision-based autonomous driving systems (e.g., Tesla and Toyota). These advancements, however, have been primarily directed towards optimizing performance, with limited consideration for security vulnerabilities. This study introduces the first backdoor attack against self-supervised MDE models. By conceptualizing backdoor attacks as a multi-task challenge, we propose a novel attack framework that employs multi-task learning to iteratively optimize the standard MDE subtask and the backdoor learning subtask. Within this framework, we design two types of backdoor attacks: Target Disappearing Backdoor Attack (TDBA) and Depth Increasing Backdoor Attack (DIBA). TDBA renders specific objects invisible to the compromised model, while DIBA dramatically inflates the depth of trigger-associated objects. Extensive evaluations on three mainstream MDE models show that TDBA can make the target model directly ignore the specific objects, with a mean depth error under 0.06 meters, while DIBA significantly increases the depth for trigger-associated objects, resulting in a mean depth error exceeding 94.2 meters. These findings highlight the urgent need for advanced security measures in the development of MDE models, to mitigate such vulnerabilities. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of scaled consensus in heterogeneous multi-agent systems (HMASs) under asynchronous Denial-of-Service (DoS) attacks. In the system structure, our exploration centers on the challenge of achieving scaled consensus in HMASs. The aim is to guarantee that the states of both first-order and second-order agents within the system reach predetermined scalar values, aligning with diverse real-world scenarios. Subsequently, the security of the system in directed communication topology under asynchronous DoS attack is also taken into account. To achieve above research objectives, an impulsive control scheme based on topology switching is proposed and a corresponding algorithm for determining the pulse moments based on the Floyd algorithm is devised. This scheme utilizes the topology switching caused by asynchronous DoS attacks as the driving force for impulsive control. Furthermore, leveraging the correlation property of random matrices, we derive the sufficient conditions for the system to achieve secure control. Finally, a numerical simulation is provided to validate the effectiveness of the protocol and the accuracy of conclusions. [ABSTRACT FROM AUTHOR]

Information exchange among individuals is one of the key factors leading to formation of modular structures in social networks, often referred as communities. In this paper, we devise a novel information diffusion based approach to leverage the latent information exchange among the individuals for identifying communities. Designed an algorithm called PraSar, which has mainly two phases namely seeding and unification. In seeding phase, closely connected groups of nodes called seed communities are identified by analyzing the cascades obtained during information diffusion. In unification phase, the external connections of seed communities are reduced by unifying two or more seed communities. Involvement of cascade that are formed during the diffusion process enables PraSar to operate locally and giving an overall complexity of O (n 2). The theoretical foundation of the proposed approach is established by various theorems. Empirical results on diverse real world datasets are evident for the effectiveness and competitiveness of the PraSar algorithm over state-of-the-art community detection algorithms. • A new community detection algorithm based on cascading is proposed. • Seed communities generated subsequently with cascading. • Novel properties proposed to address monster community formation and over-partitioning. • Experimental results show that the algorithm performs well on diverse networks. [ABSTRACT FROM AUTHOR]

• New GAN activation function based on the Smirnov transform that widely improves the quality of the generated data. • The novel GAN can faithfully replicate both continuous and discrete random variables. • The Smirnov Transform is derivable and therefore, the activation function can be seamlessly integrated in the backpropagation of a GAN training. • Only the standard activation functions of the last layer of the generator have to be substituted by the new activation functions. • There is no need to modify any additional element or layer of the GAN architecture. • The proposal was tested on one artificial and four real datasets showing a consistent improvement in quality. • Generated data is so faithful that can fully substitute real data for ML purposes. Solving the convergence issues of Generative Adversarial Networks (GANs) is one of the most outstanding problems in generative models. In this work, we propose a novel activation function to be used as output of the generator agent. This activation function is based on the Smirnov probabilistic transformation and it is specifically designed to improve the quality of the generated data. In sharp contrast to previous works, our activation function provides a more general approach that deals not only with the replication of categorical variables but with any type of data distribution (continuous or discrete). Moreover, our activation function is derivable and therefore, it can be seamlessly integrated in the backpropagation computations during the GAN training processes. To validate this approach, we firstly evaluate our proposal on two different data sets: a) an artificially rendered data set containing a mixture of discrete and continuous variables, and b) a real data set of flow-based network traffic data containing both normal connections and cryptomining attacks. In addition, three publicly available data sets were added to the evaluation to generalize the obtained results. To evaluate the fidelity of the generated data, we analyze their results both in terms of quality measures of statistical nature and regarding the use of these synthetic data to feed a nested machine learning-based classifier. The experimental results evince a clear outperformance of a Wasserstein GAN network (WGAN) tuned with this new activation function with respect to both a naïve mean-based generator and a standard WGAN. The quality of the generated data allows to fully substitute real data with synthetic data for training the nested classifier without a significant fall in the obtained accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates the event-based fully distributed secondary voltage tracking control problem for direct current (DC) microgrids under denial-of-service (DoS) attacks. Compared with the existing methods, an event-based fully distributed adaptive control scheme is proposed for the secondary control only related to the dynamics of the distributed generator (DG) itself and its neighbors' information, which can achieve the tracking of all DGs without using global information. With DoS attacks consideration, the Zeno behaviors are eliminated by proving that any triggered intervals are positive. In the event-triggered protocols, consecutive communications are not needed for updating the control law and the resources of the communication network can be saved. Finally, an example is used to verify the designed control method validly. [ABSTRACT FROM AUTHOR]

With growing opportunities and incentives to disrupt critical space infrastructure systems, cybersecurity of data pipelines associated with satellite images is becoming an increasingly important task. Combined with this threat is a growing body of literature focused on data poisoning and backdoor attacks on deep learning models tailored toward image recognition. To date, there is no work exploring backdoor attacks on deep learning models trained on satellite data. In this study, we evaluate the susceptibility of convolutional networks trained on satellite images to a common trigger-based backdoor attack. We first insert the attack into models trained on two satellite datasets and assess the susceptibility of each model to the attack. We then explore the capability of two state-of-the-art countermeasure techniques based on neuron activity to detect and repair the damage inflicted by the attack. Our results show significant and highly varied attack susceptibility ranging from 0 to 100% with strong dependence on the combination of classes targeted and features common in satellite images. Existing defense strategies are shown to be generally ineffective for this style of attack on satellite data. Finally, we introduce a diagnostic approach for the detection of poisoning vulnerability in satellite image-based deep learning approaches. [ABSTRACT FROM AUTHOR]

• A new feature set composed of 46 features for phishing URLs detection were proposed. • An updated feature selection algorithm to obtain the most representative features. • An optimum subset composed of 9 features for phishing URLs detection was proposed. • Random Forest obtains the best Accuracy results using the feature subset proposed. • The Accuracy results obtained outperforms those reported in the literature. Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). Hence, it is advantageous to detect a phishing attack by analyzing URLs before accessing them. With the raising of the Internet of Things (IoT), phishing attacks are moving to this field due to the number of IoT devices and the amount of personal information they handle. Although several approaches were proposed for phishing attacks detection, the URL-based Machine Learning approaches obtain better performance results, but all of them are dependent on the feature set used. Contradictorily, only a few works on selecting the best-suited feature set for improving the phishing detection process have been published. The present research explores how to obtain a feature set that substantially enhances the phishing detection rate in IoT environments. Hence, a feature selection algorithm was adopted and extended for getting the most representative feature set. When Random Forest is used with the proposed data representation, the phishing URL attacks discovery rate is 99.57 %. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of event-triggered remote state estimation for cyber-physical systems (CPSs) under malicious denial-of-service (DoS) attacks. A remote estimator with intermittent observations is derived, and a novel event-triggered communication strategy is designed by transmitting observations to the remote estimator when the predicted estimation error covariance exceeds a given threshold. By constraining the upper bound of the total attack ratio, sufficient conditions to guarantee the almost sure stability of the proposed estimator are presented. Furthermore, the event-triggering parameter is designed to balance the estimation performance and the network resource utilization. In contrast to the previous studies on remote state estimation with random packet losses, the considered DoS attack case is more challenging since the probabilistic property of the packet loss process is unavailable to describe the attacks. Finally, the efficiency of the presented method is illustrated by simulation results. [ABSTRACT FROM AUTHOR]

Deep learning technologies have achieved remarkable success in various tasks, ranging from computer vision, object detection to natural language processing. Unfortunately, state-of-the-art deep learning technologies are vulnerable to adversarial examples and backdoor attacks, where an adversary destroys the model's integrity. The obstacles have urged intensive research on improving the ability of deep learning technologies to resist integrity attacks. However, existing defense methods are either incomplete (i.e., only a single attack can be detected) or expensive computing resources. It requires the defense method to have universal property, which can effectively and efficiently detect multiple integrity attacks. To this end, we propose a similarity-based integrity protection method for deep learning systems (IPDLS), which is provided with the universal property. IPDLS realizes anomaly detection by measuring the similarity between suspicious samples and samples in a preset verification set. We empirically evaluate IPDLS on the MNIST and CIFAR10 datasets. Experimental results have verified the effectiveness of IPDLS, which can detect adversarial examples and backdoor attacks simultaneously. [ABSTRACT FROM AUTHOR]

• A fault diagnosis method based on memory spiking neural P systems is proposed. • A general matrix reasoning algorithm for the above P system is proposed. • The reasoning model for measurement tampering attack identification is proposed. • The reasoning model for fault diagnosis is proposed. Cyber-attacks can tamper with measurement data from physical systems via communication networks of smart grids, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section. Accordingly, a fault diagnosis method should first determine whether a fault is actually present; however, current diagnosis methods of power systems struggle to achieve this goal. This paper proposes a novel method for fault diagnosis based on memory spiking neural P systems, which can distinguish false faults caused by measurement tampering attacks. The proposed method consists of three modules with the functions of suspicious fault section detection, measurement tamper attack identification and fault diagnosis, respectively. The suspicious fault section detection module is used to find candidate sections to reduce the fault diagnosis scope. The attack identification module is designed to identify whether a possibly faulty section is under the measurement tampering attack or not. The fault diagnosis module is devised to diagnose true faults, detecting both the fault sections and their corresponding fault types. To achieve the above goals, inspired by the memory recall mechanism of human brains, a memory spiking neural P system and a corresponding general matrix reasoning algorithm are proposed, which can synthetically utilize the remote measurements and remote signals via a new modeling mechanism. Finally, case studies based on the IEEE 14 and IEEE 118 bus systems verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic event-triggered model-free adaptive control (DET-MFAC) problem for nonlinear cyber-physical systems (CPSs) subject to aperiodic denial-of-service (DoS) attacks. With the aid of the dynamic linearization technique, the nonlinear CPSs are converted into an equivalent linear data model. Then, a novel DET-MFAC framework under aperiodic DoS attacks is established, in which the DET mechanism is applied to save network resources. Based on the established DET-MFAC framework, a novel DET-MFAC scheme is proposed to track the reference trajectory under aperiodic DoS attacks. The main advantage of the designed control scheme is that only the input and output data of the system are used and the structure of the system is unknown for the designer, which makes the result with good robustness. Finally, two simulations are shown to testify the effectiveness of the designed DET-MFAC scheme. [ABSTRACT FROM AUTHOR]

This paper is devoted to investigate the dynamical behaviors of malware attack on a familiar kind of complex heterogeneous networks, namely Wireless Sensor Network, and discuss an effective immunization treatment based on fractional interconnected Takagi-Sugeno (T-S) fuzzy systems. Our approach is based on the mathematical modeling to establish a controlled fractional network-based SIRS malware propagation model that better describes the attacking behavior of malicious objects on Wireless Sensor Network. After that, we point out some qualitative properties of the proposed network-based SIRS malware propagation model such as the existence of positively invariant set, backward bifurcation and asymptotic behavior. Especially, in order to study the model's stability, we evaluate an epidemiological threshold value R 0 , namely basic reproductive ratio, which ensures the existence of at least one endemic equilibrium P ⁎ and the local asymptotic stability of malware-free equilibrium P 0. As a consequence of theoretical result, the malware-free equilibrium P 0 is unstable when R 0 > 1 and hence, the rest of this paper is to address a stabilization problem for the proposed controlled network-based model and establish some sufficient conditions related to linear matrix inequalities and positive definite matrices. Finally, we illustrate the obtained theoretical results by a computational example. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of adaptive event-triggered anti-synchronization control for bidirectional associative memory neural networks subject to energy-limited denial of service attacks. First, a novel adaptive event-triggered scheme is developed by resorting to the acknowledgment character technique, which can help conserve valuable communication resources and has better performance in resisting malicious cyber attacks compared to traditional schemes. Second, a more general attack strategy for denial of service attacks is proposed with the consideration of energy constraints, and an anti-synchronization error system is established to analyze the anti-synchronization behavior. Then, sufficient conditions are provided to guarantee the anti-synchronization of drive and response bidirectional associative memory neural networks in H ∞ sense. Next, a design approach is obtained based on the above conditions for the controller gains. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed method and its superiority over the traditional event-triggered scheme. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL substantially reduces privacy concerns and alleviates the isolated data problem. However, in reality, the success of FL is predominantly attributed to a centralized framework called FedAvg [1] , in which workers are responsible for model training, and servers are in control of model aggregation. Nevertheless, FedAvg's centralized worker-server architecture has raised new concerns, including low scalability of the cluster, risk of data leakage, and central server failure or even defection. To overcome these challenges, we propose De centralized F ederated T rusted A veraging (DeFTA), a decentralized FL framework that serves as a plug-and-play replacement for FedAvg , bringing instant improvements to security, scalability, and fault-tolerance in the federated learning process. In essence, it primarily consists of a novel model aggregating formula with theoretical performance analysis, and a decentralized trust system (DTS) to significantly enhance system robustness. Extensive experiments conducted on six datasets and six basic models suggest that DeFTA not only exhibits comparable performance with FedAvg in a more realistic setting, but also achieves remarkable resilience even when 67% of workers are malicious. • A novel model aggregating formula is proposed that eliminates the aggregating bias in decentralized FL. • Selfish workers are introduced to the decentralized FL setting, which is simple and robust against backdoor attacks. • A general decentralized FL framework is proposed that features a drop-in replacement for the centralized FedAvg algorithm. • This replacement leads to improvements in data security and system reliability, with negligible model performance loss. [ABSTRACT FROM AUTHOR]

This article addresses the security synchronization issue of complex dynamical networks under asynchronous cyber and physical attacks within the context of cyber-physical systems. An observer is devised to estimate the system's state using the measured outputs of partial nodes. The integration of a buffer for storing historical information enhances the accuracy of state estimation under asynchronous attacks, enabling the observer-based control scheme to generate more precise control signals during attacks. The robustness against external disturbances is reinforced by introducing disturbance compensation for both the observer and the controller. Additionally, an adaptive memory event-triggered mechanism is designed to conserve network resources. Then, a sufficient condition for the synchronization of complex dynamical networks against asynchronous attacks is obtained, and the gains of the observer and controller are computed based on the linear matrix inequality technique. Finally, a simulated example demonstrates the effectiveness of the suggested strategy. [ABSTRACT FROM AUTHOR]

Public key encryption with equality test (PKEET) is a novel primitive which supports equality comparisons on two encrypted messages. Currently, most of the existing PKEET schemes are based on bilinear pairing and require heavy computational overheads. To address this issue, Shen et al. recently proposed an efficient group public key encryption supporting equality test without bilinear pairings scheme. Compared with other schemes, their scheme reduces the usage of expensive bilinear pairing operations and enjoys higher computation efficiency. They claimed that their scheme achieved one-wayness security in the random oracle model and resisted offline message recovery attack. In this letter, we analyze Shen et al.'s scheme through two concrete attacks and demonstrate that their scheme can not support the above two security requirements. An improved scheme is provided to overcome the security vulnerabilities in their scheme. Performance analysis shows that our improved scheme has certain advantages in both computation overhead and storage overhead. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggering mechanism (ETM) and a predictor compensation control strategy are proposed for cyber-physical systems (CPSs) with stochastic disturbance under malicious denial-of-service (DoS) attacks. First, a novel ETM excluded Zeno behaviour for CPSs is designed without considering DoS attacks, and the input-to-state stability (ISS) of the closed-loop systems is guaranteed. Second, given that DoS attacks could block the transmission of information, a predictor is used to compensate for the lost signals of CPSs. Then, a control strategy is proposed by combining the ETM and predictor, which can reduce the times of data transmission and control updates. Furthermore, a sufficient condition is derived to guarantee the ISS of the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by an example. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing the distributed sampled-data H ∞ consensus filters for cyber-physical systems when the sampling rate of the sensors is different from that of the filters, and the measurement channels from the sensors to the filters and the communication links between the filters are subject to the denial-of-service attacks. First, a new sequence is defined to unify the sampling instants of the sensors and filters. Based on this new sequence and the detectability decomposition, distributed multi-rate sampled-data H ∞ consensus filters are designed, and the resilience of the proposed filters to the denial-of-service attacks is analyzed. It is shown that the attacks on the measurement channels will degrade the performance of the state estimations of both detectable part and undetectable part of the system state, while the attacks on the communication links only affect the performance of the state estimations of the undetectable part of the system state. At last, an example is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates an event triggered control problem of connected vehicles under multiple cyber-attacks, including denial-of-service attacks (DoS) and deception attacks, which occur aperiodically. Then, a novel event triggered control scheme is proposed in the presence of multiple cyber-attacks. The upper bound of attack frequency, attack length rate of DoS attacks and the conditions of controller gains are obtained. It is proven that the proposed distributed control scheme can realize the asymptotic tracking performance of connected vehicles while avoiding Zeno behavior. A string stability analysis under multiple cyber-attacks is also provided. Simulation results confirm the effectiveness of the event triggered control schemes under multiple cyber-attacks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the leader tracking problem of a directed networked Lagrangian systems under denial-of-service (DoS) attacks on communication links. To this end, a new adaptive distributed resilient control scheme is proposed to resist the influence of the DoS attacks. Under the proposed control framework, the local controllers are allowed to update their control signals aperiodically and asynchronously. The resulting system is analyzed based on an improved multidimensional small gain approach. Under the derived sufficient conditions, which significantly relax the existing convergence conditions that depend on the duration and frequency of DoS attacks for linear systems, the proposed control scheme can almost ensure the resilient leader tracking under arbitrary DoS attacks with bounded durations and frequencies. [ABSTRACT FROM AUTHOR]

With the widespread application of communication networks in industrial control systems, cyber-attacks bring a huge threat to the networked control system. In this paper, the issue of secure control of nonlinear jump networked systems with false data inject (FDI) attacks is investigated. In order to against the malicious attacks, a reduced-order-asynchronous sliding-mode control (SMC) strategy is proposed. Firstly, in order to damp the effect of nonlinear term and FDI attacks, an asynchronous sliding-mode function is designed, and the resultant sliding-mode dynamics is generated by using the reduced-order method. Then, the stability conditions for the reduced-order sliding-mode dynamics are given, and the sliding-mode parameters are solved by linear matrix inequality technology. Furthermore, the sliding-mode controllers are designed under the situation of the parameters is known or unknown, respectively, and the reachability of the sliding-mode motion is certificated. Finally, two examples are implemented to illustrate the potential of the presented control strategy. [ABSTRACT FROM AUTHOR]