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The increasing reliance on internet-based services has heightened the vulnerability of network infrastructure to cyberattacks, particularly distributed denial of service (DDoS) attacks. These attacks can cause severe disruptions and significant financial losses. Early detection of malicious traffic is crucial in effectively combating such threats. This paper presents an innovative approach called the Encoder-Stacked deep neural networks (E-SDNN) model, which leverages Stacked/bagged multi-layer perceptrons (MLP) for accurate DDoS attack detection. The proposed method employs an encoder to select pertinent features from a preprocessed dataset, enabling precise attack detection. Extensive experiments were conducted on benchmark cybersecurity datasets, namely CICDS2017 and CICDDoS2019, encompassing various DDoS attack scenarios. The experimental results demonstrate the superiority of the E-SDNN model compared to state-of-the-art methods. The proposed E-SDNN model achieved an impressive overall accuracy rate of 99.94% and 98.86% for CICDDS2017 and CICDDoS2019, respectively. [ABSTRACT FROM AUTHOR]

Summary: This article considers the parameter identification problem for the finite impulse response system based on binary observations under denial of service attacks. First, in addition to designing the identification algorithm for the unknown parameter, and its convergence is verified, simultaneously obtaining the asymptotic normality. Then, based on the convergence speed of the identification algorithm and the covariance matrix of the estimation error, the optimal attack strategy problem is transformed into an optimization problem with constraints, then the optimal solution is given. Furthermore, a defense strategy with dual time‐scale input design method is proposed and its effectiveness is demonstrated. Finally, numerical simulations are applied to evidence the correctness of the raised method. [ABSTRACT FROM AUTHOR]

There are some attacks on the network, such as botnet attack, DDoS attack and Local Area Network Denial attack, which are attacked on certain group of clustered nodes in the network. At present, the existing connectivity has certain defects in reflecting the fault-tolerant ability of the network under these network attacks. In order to measure the fault tolerance and reliability of a network which is attacked on certain group of clustered nodes in the network by attacker, Lin et al. (IEEE Trans Comput 70:1719–1731, 2021) proposed the g-faulty-block connectivity. A subset F ⊆ V (G) is called a g-faulty-block of a graph G if G - F is disconnected, each component of it has at least g + 1 vertices and the subgraph induced by F is connected. The cardinality of a minimum g-faulty-block of G, denoted by FB κ g (G) , is the g-faulty-block connectivity of G. Larger h-fault block connectivity means that an attacker must launch an attack on a larger block of connected nodes so that each remaining component is not too small, which in turn limits the size of the larger components. The larger the h-fault block, the more difficult it is for an attacker to accomplish this goal. In this paper, we obtain FB κ 0 (FQ n) = 2 n + 1 , FB κ 1 (FQ n) = 3 n - 1 and FB κ g (FQ n) = (g + 2) n - 3 g + 3 for 2 ≤ g ≤ n - 4 and n ≥ 7 , where FQ n is n-dimension folded hypercube. [ABSTRACT FROM AUTHOR]

In this article, by considering the existence of unavailable state, external disturbance, and denial of service (DoS) attacks, an anti‐disturbance trajectory tracking controller is proposed for the air‐ground system composed of unmanned autonomous helicopter (UAH) and unmanned ground vehicle (UGV). Initially, by combining the advantages of conventional event‐triggered scheme (ETS) and memory ETS, a switching‐like event‐triggered mechanism is put forward, which can induce less data transmissions without reducing control performance, and effectively restrain the DoS attacks. Second, by dividing the DoS attacks into active intervals and sleep ones, the concept of acknowledgement character technology (ACK) is presented to define the suitable types of time intervals. Third, a switching‐like dynamic ETS is introduced for the sleep intervals of DoS attacks while the ETS with fixed triggered‐threshold is exploited for the active intervals. Fourth, based on the occurrence of DoS attacks, a switching‐like observer and a normal one are respectively proposed to estimate the UGV state, the UAH state, the reference input of the UGV, and the disturbance of the UAH, which are further utilized to design the tracking controller. Fifth, an augmented closed‐loop system consisting of observation errors and tracking error is established and its stability is analyzed by using Lyapunov stability theory. Then, a sufficient condition on co‐designing the parameters of switching‐like ETS, observers, and tracking controller is presented in terms of linear matrix inequalities (LMI). Finally, the validity and superiority of the proposed control scheme are verified by resorting to simulations and comparisons. [ABSTRACT FROM AUTHOR]

Summary: This paper designs a distributed dynamic‐detection‐based event‐triggered control method to address the secure tracking control problem. A distributed online iterative dynamic detection mechanism is designed to detect denial‐of‐service (DoS) attacks. The detection results are transmitted into a proposed ET method to adjust the triggering threshold, which can mitigate the impact of DoS attacks. Moreover, the signals of state, observations of disturbance observers and adaptive laws are calculated to generate the trigger signals in the control signal simultaneously, which can help conserve communication resources and reduce the occurrence of pulse phenomenon. Meanwhile, since the predictor exists certain prediction errors that will reduce the tracking performance, a speed function is introduced to ensure the achievement of the control objective. The designed solution guarantees semiglobally uniformly ultimately bounded for all signals within the closed‐loop system. Finally, the effectiveness of control scheme is demonstrated through several simulation results. [ABSTRACT FROM AUTHOR]

The architecture of software‐defined network (SDN)enhances the openness of the network by separating the control and forwarding functions, but the centralized SDN control form is susceptible to distributed denial of service (DDoS) attacks. In this paper, entropy value and back‐propagation neural network (BPNN) were applied to the DDoS attack detection of SDN, and then the two detection algorithms were simulated in MATLAB software and compared with the K‐means algorithm. The results showed that in the face of four DDoS attacks, SYN Flood, ACK Flood, UDP Flood and ICMP Flood, the BPNN‐based DDoS detection had higher accuracy and less detection time; the switch that adopted the BPNN‐based DDoS detection algorithm adjusted the traffic ratio back to normal level faster when facing DDoS attacks, reducing the impact on other switches and maintaining the traffic stability of the network. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The exponential increase in Internet-facing devices in the last decade has resulted in IP address exhaustion due to the limitations of the existing IPv4 address space. Therefore, the Internet Engineering Task Force engineered a new version of the Internet protocol known as Internet Protocol Version 6 (IPv6) to resolve the issue. However, IPv6 is highly dependent on the neighbor discovery protocol (NDP), which, unfortunately, has well-known vulnerabilities in its underlying messaging protocol, the Internet Control Message Protocol version 6. So, the NDP flaws leave the IPv6 network open to many security threats and attacks, including man-in-the-middle, spoofing, and denial-of-service attacks, which are the most annoying attack at the network layer. Unfortunately, one of the critical issues plaguing the existing anomaly-based detection system is the effectiveness of detecting NDP-based DDoS attacks, which requires urgent attention. This paper suggests a system to find network traffic patterns that are not normal that are caused by NDP-based attacks. It does this by teaching neural networks how to recognize network attack patterns using the backpropagation algorithm. The proposed system is a big step forward from where the field is now because it uses a complex neural network algorithm to create an NDP anomaly-based detection system. Using a real dataset to test the proposed system's performance shows that it can find NDP anomalies with a 99.95% success rate, a 99.92% precision rate, a 99.98% recall rate, an F1-Score of 99.98%, and a 0.040% false positive rate. Also, the proposed approach shows better results compared to other existing approaches. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) encompasses smart devices, manufacturing systems, humans, and networks for automated productive outcomes. The placement of devices and networks is vulnerable to distributed denial of service (DDOS) attacks that degrade the productivity and efficiency of IIoT. In this article, we propose a checkpoint‐intrigued adversary mitigation scheme (CIAMS) for improving the security features and recommendations of the detection systems. Features that use the recommendation to provide relevant information maintain their security level. A DDoS attack is dealt with at the outset, resulting in increased productivity. The IIoT's smart devices are less productive and efficient because of this DDoS attack. This CIAMS method is designed to address vulnerability and the ability to survive the features checkpoints. The proposed scheme substantiates the security breach and lag in the checkpoint systems against DDOS attacks. The checkpoints' vulnerability level and surviving features are assessed using a classified learning approach. In this assessment, the degrading features are reimbursed by improving the security functions, control, and access methods. Periodic checkpoint replacement and mutual security measures are used for mitigating the prolonging DDOS impact in the network. The proposed scheme's performance is verified using false positives, service distribution, lag, an efficiency score. Improvements have been made to the industrial environment's service delivery and efficiency. By reducing false positives by 10.35%, the proposed scheme improves service distribution ratio and efficiency score by 11.68% and 12.55% for different devices. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is a paradigm shift in the network industry with decoupling of control and data plane. This helps network engineers to control and manage their network from centralized controller and troubleshoot the network at much better pace. With the benefits that SDN provides, it also has some vulnerable to attacks like Distributed Denial of Service (DDoS) that can disrupt the availability of the network. Most of the recent research work in DDoS detection and mitigation is done over Traditional network environments and packet limit strategy is used for mitigation by researchers with very less focus on redirection of illegitimate traffic which can result in less downtime and helpful in network analysis. The major contribution of this paper is detection and mitigation of DDoS in Software Defined Networks. We have created our own DDoS dataset with over 1.7 million entries. For Detection, we have used two different methods: (1) Snort (IDS) and (2) Machine Learning. In Machine Learning, we have used eight different Machine Learning algorithms that also include Ensemble Classifier and a Hybrid method of SVM-RF. We were able to get 99.1% accuracy which is a substantial growth as compared to recent works. For Mitigation, we have again used two methods: (1) Drop illegitimate traffic and (2) Redirect Illegitimate traffic. Results showed that hybrid SVM-RF algorithm works better as compared to individual Machine Learning algorithms and also as mitigation depends on Detection accuracy, so, the better the detection, the better the mitigation. In this paper detection and mitigation of DDoS has been performed over a SDN network. In which We have created our own dataset with over 1.7 million entries. For detection we have used two method: Snort (IDS), and Machine Learning and for Mitigation, Drop illegitimate traffic, and Redirect Illegitimate traffic. [ABSTRACT FROM AUTHOR]

In this article, a resilient model predictive control algorithm incorporating a dual‐mode control strategy is proposed for a class of cyber‐physical systems subject to state and input constraints, additive disturbances and denial‐of‐service (DoS) attacks. An adversary aims to disrupt the controller‐to‐actuator channel by deploying adversarial jamming signals. By exploring the prediction uncertainty between a nominal model and an actual model and utilizing a constraint tightening technique, a novel set of constraints is developed, which guarantees robust constraint satisfaction in open‐loop mode caused by DoS attacks. A robust positive invariant set is designed, which allows the control mode to switch from rolling optimization to a state‐feedback control law when the state enters its interior, thus saving computational resources. We explicitly determine a critical duration for DoS attacks and, through theoretical analysis, establish that when the duration of DoS attacks falls below this critical value, the proposed algorithm ensures guaranteed recursive feasibility and robust asymptotic stability. Finally, some comparisons are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

This article investigates the problem of secure memory adaptive event‐triggered filter design for nonlinear network systems under hybrid network attacks. First, the introduction of memory adaptive event‐triggered mechanism and numerical quantization is employed to enhance system performance, optimize network channel utilization, and prevent network congestion. Second, we consider the impact of deceptive attacks and DoS attacks occurring in the network channel on data transmission, which form the hybrid network attacks studied in this article. Subsequently, based on the established error model, we derive sufficient conditions for the mean‐square exponential stability of the system under a given H∞$$ H\infty $$ performance index using Lyapunov–Krasovskii function and linear matrix inequality (LMI) techniques. Furthermore, the filter parameters are determined using the LMI method. Finally, we illustrate the feasibility and generality of the conclusions through numerical simulation examples and a tunnel diode circuit system. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks. [ABSTRACT FROM AUTHOR]

Denial of Service (DoS)-Oriented cyber attack has been a major threat for physical security in many kinds of network media, including the Internet of Vehicles (IoV). This paper focuses on the scenario of IoV, and proposes a machine learning-based fast detection method for adversarial neural network-based fast detection method for DoS-oriented cyber attacks. First, by analyzing the implementation principles and attack characteristics of three attack types, three aspects of statistical features are extracted: maximum matching packet growth rate, source address entropy value, and flow table similarity. Then, they are used as the input features to establish an adversarial machine learning-based DoS cyber attack detection method. On this basis, the field features of six stream rules are extracted, and two DoS cyber attack detection methods via machine learning are formulated. The proposals are able to detect the low-rate DoS-based cyber attacks against the data layer. The experimental results show that the proposed DoS attack detection method based on machine learning can effectively detect three DoS attacks under IoV, and these two algorithms have higher detection rates when compared with other algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing secure control for networked multi-agent systems (MASs) under Denial-of-Service (DoS) attacks. We propose a constructive design method based on the interaction topology. The MAS with a non-attack communication topology, modeled by quasi-Abelian Cayley graphs subject to DoS attacks, can be represented as a switched system. Using switching theory, we provide easily implementable sufficient conditions for the networked MAS to remain asymptotically stable despite DoS attacks. Our results are applicable to both continuous-time and discrete-time systems, as well as to discrete-time systems with variable steps or systems that combine discrete and continuous times. [ABSTRACT FROM AUTHOR]

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H − / H ∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H ∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR). [ABSTRACT FROM AUTHOR]

Summary: DDoS attacks are a type of cloud incursion that lessen service degradation. DDoS attacks target the cloud network with invalid requests, rejecting legitimate requests. Such attacks disrupt the entire cloud architecture, thus it needs efficient detection methods to spot their presence. This study proposes a novel ensemble classification model for DDoS incursion detection. Pre‐processing, feature extraction, and attack detection are the three main components of the suggested intrusion detection system. Improved data imbalance processing is processed during pre‐processing. Features including HOS‐based features enhanced entropy‐based features, correlation features, and raw features are extracted from the pre‐processed data. The generated features are trained using an ensemble model, which integrates classifiers like SVM, RF, NN, LSTM, and DRN, during the attack detection phase. A new hybrid approach known as TUDMA optimally trains the model by setting the ideal weight because DRN provides the final detected outcome. By reducing errors, this ideal training will guarantee an improvement in detection results. The suggested hybrid optimization combines the two techniques, TDO and DMO. The accuracy of the methods FS‐WOA‐DNN, RBF‐PSO, SMA, BRO, SLO, and NMRA was 86.73%, 85.69%, 84.58%, 87.43%, 88.91%, and 89.78%, respectively, while the accuracy of the TUDMA was 95.34% in the 80th learning percentage. Finally, for many measurements, the suggested efficiency is superior to the traditional methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as an inevitable part of human life, that includes online learning, smart homes, smart cars, smart grids, smart cities, agriculture, and e-healthcare. It allows us to operate them 24/7 from anywhere. These smart IoT devices streamline our daily lives by automating everything around us. Several security issues have arisen with the continuous growth of non-secure IoT devices. Distributed Denial of Service (DDoS) attack is one of the most prominent security threats to Internet-based services and IoT platforms. It has the potential to break down the victim's server or network by transferring an immense amount of irrelevant traffic from the pool of compromised IoT devices. In this article, we present: (i) A comprehensive cyberattacks taxonomy for IoT platforms, (ii) Systematically demonstrate IoT technology: evolution, applications, and challenges, (iv) Systematic review of existing machine learning (ML) and deep learning (DL)-based detection approaches for large-scale IoT traffic-based DDoS attacks, (v) Characterize publicly available IoT-traffic-specific datasets, and (vi) Discuss various open research issues with possible solutions for detecting IoT traffic-based DDoS attacks, including future directions. [ABSTRACT FROM AUTHOR]

DDoS attacks launched by IoT botnets can be classified into low-rate and high-rate DDoS attacks, which depict two distinct scenarios of data imbalance, namely, the minimal and maximal proportion of attack traffic. Developing a detection model that can effectively address two data imbalance scenarios concurrently is crucial in safeguarding computers against DDoS attacks. This necessitates the development of a model with enhanced generalizability. However, in the examination of cross-evaluation across datasets which is closely associated with the generalizability of models, there is a limited focus on addressing the issue of data imbalance. This oversight has led to a significant decrease in model performance when compared to training and testing on a single dataset. To identify guiding principles within this context, the literature suggests employing an undersampling technique on benign instances during the preprocessing phase to eliminate redundant data. Additionally, the literature conducts simulations of various cross-evaluation scenarios and evaluates the performance of classifiers. This evaluation is done after applying representative oversamplers, undersamplers, and the method proposed in this paper. After analyzing the experiment results, it is advisable to employ oversamplers for low-rate DDoS datasets and undersamplers for high-rate DDoS datasets. Moreover, optimizing SMOTE-based algorithms for specific models will yield optimal performance. [ABSTRACT FROM AUTHOR]

This paper considers the security bipartite synchronization problem of nonlinear multi-agent systems (MASs) under the impact of denial-of-service (DoS) attacks. Notice that malicious attackers may block the neighbor information transmission among agents, which will reduce the efficiency of the distributed control strategy and even paralyze the entire system. In order to eliminate the impact of DoS attacks on the stability of MASs, this paper represents the first attempt to solve the security bipartite synchronization problem with the help of a distributed leader state observer. An observer that can obtain the leader state estimation is established, and the control strategy constructed based on the observer can eliminate the impact of DoS attacks. Theoretical analysis shows that the control strategy proposed in this paper can realize the security bipartite synchronization of the system under the impact of DoS attacks. The validity of the proposed results is verified by several presented simulation results. [ABSTRACT FROM AUTHOR]

Security and efficiency remain a serious concern for Internet of Things (IoT) environments due to the resource-constrained nature and wireless communication. Traditional schemes are based on the main mathematical operations, including pairing, pairing-based scalar multiplication, bilinear pairing, exponential operations, elliptic curve scalar multiplication, and point multiplication operations. These traditional operands are cost-intensive and require high computing power and bandwidth overload, thus affecting efficiency. Due to the cost-intensive nature and high resource requirements, traditional approaches are not feasible and are unsuitable for resource-limited IoT devices. Furthermore, the lack of essential security attributes in traditional schemes, such as unforgeability, public verifiability, non-repudiation, forward secrecy, and resistance to denial-of-service attacks, puts data security at high risk. To overcome these challenges, we have introduced a novel signcryption algorithm based on hyperelliptic curve divisor multiplication, which is much faster than other traditional mathematical operations. Hence, the proposed methodology is based on a hyperelliptic curve, due to which it has enhanced security with smaller key sizes that reduce computational complexity by 38.16% and communication complexity by 62.5%, providing a well-balanced solution by utilizing few resources while meeting the security and efficiency requirements of resource-constrained devices. The proposed strategy also involves formal security validation, which provides confidence for the proposed methodology in practical implementations. [ABSTRACT FROM AUTHOR]

This paper investigates the secure defense control problem for memristive recurrent neural networks (MRNNs) under denial-of-service (DoS) attacks. Based on quantizer and logical processor techniques, a new secure defense control strategy is designed to maintain the performance of MRNNs subject to DoS attacks. The secure defense control strategy can not only effectively capture information about the dwell time of each DoS attack, but also can effectively reduce the amount of data to be transmitted and save communication resources. By constructing a Lyapunov–Krasovskii functional that depends on sampled information at t k and t k + 1 , some new stability criteria are derived for MRNNs under DoS attacks. By solving a set of LMIs, the control gain of the quantized sampled-data controller can be obtained. Thus, the corresponding secure quantized sampled-data controller is designed for MRNNs under DoS attacks. When there is no quantizer, the secure defense control problem for MRNNs under DoS attacks is also studied. Two numerical examples are given to confirm the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

In this article, the resilient leaderless consensus problem for a multi‐agent system (MAS) under denial‐of‐service(DoS) attack is investigated. The DoS attack is carried out with multiple strategies. The sufficient condition for MASs achieving consensus under multi‐mode DoS attack is developed. In order to actively alleviate the influence caused by the DoS attack, the MAS switches between normal and secure modes. Once a DoS attack occurs, the agents will switch into the secure mode with a lower open‐loop divergence rate. A dynamic event‐based consensus protocol is proposed, driving the MAS achieve consensus while saving communication resources effectively. Moreover, rigorous proof analysis demonstrates the Zeno‐free property of the developed dynamic event‐triggered mechanism. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This work investigates the ℋ∞$$ {\mathscr{H}}_{\infty } $$ control problem of discrete‐time singular Markov jump systems against denial of service attacks, in which the Markov state information is seen as restricted access. To solve this situation, a hidden Markov model is introduced. The main objective is to construct a controller with the help of hidden Markov model such that the stochastically admissible of the closed‐loop singular Markov jump systems with limited access mode information can be guaranteed under denial of service attacks. To produce the needed hidden Markov model‐based controller, a matrix contract transformation approach is developed. At the end, a numerical example and a tunnel diode circuit are presented to demonstrate the effectiveness and benefit of the design technique described in this research. [ABSTRACT FROM AUTHOR]

Summary: This study investigates the ℋ∞$$ {\mathscr{H}}_{\infty } $$ secure consensus issue for Markov jump multi‐agent systems with denial‐of‐service attacks and disturbance. Considering that the mode of the system cannot be directly obtained in the actual situation, a detector is constructed to obtain the mode of the system indirectly with the help of the hidden Markov model. The network communications between agents are undirected, and denial‐of‐service attacks are encountered randomly and represented by a random variate obeying Bernoulli distribution. The reduced‐order decomposed system is obtained by the method of model reduction. On this basis, the Lyapunov stability theory is applied to derive some sufficient conditions to guarantee that the system achieves consensus with a stipulated ℋ∞$$ {\mathscr{H}}_{\infty } $$ performance index. Finally, a numerical example is employed to verify the validity of the proposed protocol. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the secure group consensus is investigated via periodic event‐triggered output feedback control for nonlinear multi‐agent systems subject to multiple attacks, where replay attacks and denial‐of‐service attacks are considered simultaneously. To avoid uninterrupted communication between agents, a novel fixed/switching continuous‐discrete compensator is first put forward by introducing a discrete‐time triggering mechanism in relative output signals. Then, with the help of the gain control technique, a compensator‐based periodic event‐triggered secure control protocol is established, which includes a triggering mechanism and an output feedback controller that both are discrete‐time. Particularly, Zeno behavior and continuous monitoring of the triggering function are naturally avoided compared with existing event‐triggered control protocols. Further, by combining the Lyapunov‐Krasovskii functional approach with switching topology theory, the secure group consensus is successfully achieved under the designed secure control protocol, meanwhile, the negative effect of multiple attacks on the system is compensated. Finally, two simulation examples are applied to verify the effectiveness of proposed control protocols. [ABSTRACT FROM AUTHOR]

This article examines resource scheduling optimization for cyber‐physical systems that include a smart sensor, a remote estimator, and an attacker. The attacker's objective is to use denial‐of‐service (DoS) attack to jam the wireless channel, causing packet loss and reducing system performance. We consider a more realistic scenario where other interfering sources exist and conduct research based on the signal‐to‐interference‐plus‐noise ratio (SINR) model. Through this model, we investigate two cases: constant high or low interference power and time‐varying interference power. Different levels of attack power lead to varying packet loss rates, and the same attack power may result in different packet loss rates in different interference environments. Therefore, the attacker needs to consider the impact of other interferences to find the optimal attack strategy that increases the estimation error of the information‐physical system while reducing energy consumption. We describe this optimization problem in a Markov decision process (MDP) framework and demonstrate the existence of an optimal deterministic Markovian strategy. We also establish the monotonicity of the optimal strategy in two different cases and investigate the extreme case. Simulation results support our theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a model‐free adaptive iterative learning controller(MFAILC) based on sampled‐data under the presence of denial‐of‐service(DoS) attacks in nonlinear networked control systems. First, the MFAILC is presented only using I/O data, where a compensation mechanism for DoS attacks is proposed. With dynamic linearization techniques, the nonlinear system is transformed into a linear system in the iteration domain. Then an improved MFAILC is designed to actively compensate the lost data caused by DoS attacks, where the estimation of pseudo‐partial derivative (PPD) is improved by establishing the AR model. The proposed algorithm can weaken the adverse effects of the DoS attacks and ensure the excellent tracking performance of the system. Finally, the stability of the method is proved, and the effectiveness of the proposed algorithm is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput. [ABSTRACT FROM AUTHOR]

Software-defined networking will be a critical component of the networking domain as it transitions from a standard networking design to an automation network. To meet the needs of the current scenario, this architecture redesign becomes mandatory. Besides, machine learning (ML) and deep learning (DL) techniques provide a significant solution in network attack detection, traffic classification, etc. The DDoS attack is still wreaking havoc. Previous work for DDoS attack detection in SDN has not yielded significant results, so the author has used the most recent deep learning technique to detect the attacks. In this paper, we aim to classify the network traffic into normal and malicious classes based on features in the available dataset by using various deep learning techniques. TCP, UDP, and ICMP traffic are considered normal; however, malicious traffic includes TCP Syn Attack, UDP Flood, and ICMP Flood, all of which are DDoS attack traffic. The major contribution of this paper is the identification of novel features for DDoS attack detection. Novel features are logged into the CSV file to create the dataset, and machine learning algorithms are trained on the created SDN dataset. Various work which has already been done for DDoS attack detection either used a non-SDN dataset or the research data is not made public. A novel hybrid machine learning model is utilized to perform the classification. The dataset used by the ML/DL algorithms is a collection of public datasets on DDoS attacks as well as an experimental DDoS dataset generated by us and publicly available on the Mendeley Data repository. A Python application performs the classification of traffic into one of the classes. From the various classifiers used, the accuracy score of 99.75% is achieved with Stacked Auto-Encoder Multi-layer Perceptron (SAE-MLP). To measure the effectiveness of the SDN-DDoS dataset, the other publicly available datasets are also evaluated against the same deep learning algorithms, and traffic classification accuracy is found to be significantly higher with the SDN-DDoS dataset. The attack detection time of 216.39 s also serve as experimental evidence. [ABSTRACT FROM AUTHOR]

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attacks and packet loss. Firstly, the stochastic characteristics of DoS attacks and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of the systems. Then, by constructing a general common Lyapunov functional and combining it with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through two examples. [ABSTRACT FROM AUTHOR]

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency. Additionally, other existing solutions are vulnerable to malicious attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, and the failure of Key Forward Secrecy (KFS). To address this challenge, this paper proposes an efficient and secure handover authentication protocol for a group of MTCDs supported by blockchain technology. This protocol leverages the decentralized nature of blockchain technology and combines it with certificateless aggregate signatures to mutually authenticate the identity of a base station and a group of MTCDs. This approach can reduce signaling overhead and avoid key escrow while significantly lowering the risk associated with single points of failure. Additionally, the protocol protects device anonymity by encrypting device identities with temporary anonymous identity markers with the Elliptic Curve Diffie–Hellman (ECDH) to abandon serial numbers to prevent linkage attacks. The resilience of the proposed protocol against predominant malicious attacks has been rigorously validated through the application of the BAN logic and Scyther tool, underscoring its robust security attributes. Furthermore, compared to the existing solutions, the proposed protocol significantly reduces the authentication cost for a group of MTCDs during handover, while ensuring security, demonstrating commendable efficiency. [ABSTRACT FROM AUTHOR]

The proliferation of software-defined networks (SDN) increases the necessity of security and forensic research in this field. Network forensics is of particular importance considering the ever-increasing traffic density and variety of devices, and SDN has great potential for improved forensic processes thanks to its ability to provide a centralized view and control of the network. This article's motivation is the lack of a standard forensic process in SDN. The main objective of this study is to examine the differences in the forensic processes of different SDN controllers, whether the southbound interface data is sufficient for the forensic processes, and whether it is possible to choose the best controller in terms of forensics. Four of the most widely used controllers have been selected and tested under seven different scenarios to observe how the results were obtained in terms of forensics. During the tests, in addition to the routine data accesses, attack preparation tools and denial-of-service attack tools were used to expand the scope. Experiments in which each scenario was applied for four different controllers demonstrated that different controllers have different characteristics in network forensics parameters, such as attack type detection, attacker information, service interruptions, packet size, and the number of packets. Experiments proved that southbound interface data is sufficient for forensic processes, different controllers have different characteristics in forensic processes, none of the most used controllers is the best to cover all forensic processes, and a standard forensic method is required for software-defined network forensics. [ABSTRACT FROM AUTHOR]

Smart agriculture is a popular domain due to its intensified growth in recent times. This domain aggregates the advantages of several computing technologies, where the IoT is the most popular and beneficial. In this work, a novel and effective deep learning-based framework is developed to detect intrusions in smart farming systems. The architecture is three-tier, with the first tier being the sensor layer, which involves the placement of sensors in agricultural areas. The second tier is the Fog Computing Layer (FCL), which consists of Fog nodes, and the proposed IDS is implemented in each Fog node. The gathered information is transferred to this fog layer for further data analysis. The third tier is the cloud computing layer, which provides data storage and end-to-end services. The proposed model includes a fused CNN model with the bidirectional gated recurrent unit (Bi-GRU) model to detect and classify intruders. An attention mechanism is included within the BiGRU model to find the key features responsible for identifying the DDoS attack. In addition, the accuracy of the classification model is improved by using a nature-inspired meta-heuristic optimization algorithm called the Wild Horse Optimization (WHO) algorithm. The last layer is the cloud layer, which collects data from fog nodes and offers storage services. The proposed system will be implemented in the Python platform, using ToN-IoT and APA-DDoS attack datasets for assessment. The proposed system outperforms the existing methods in accuracy (99.35%), detection rate (98.99%), precision (99.9%) and F-Score (99.08%) for the APA DDoS attack dataset and the achieved accuracy of the ToN-IoT dataset (99.71%), detection rate (99.02%), precision (99.89%) and F-score (99.05%). [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has shown incredible adaptability in recent years and has become an integral part of human life. The proliferation of IoT technology has made IoT devices more prone to severe security threats, such as Distributed Denial of Service (DDoS) attacks, which are dangerous threats to public systems and networks. Further, the frequency and complexity of IoT traffic-based DDoS attacks are increasing year-by-year. This article proposes an IoT traffic-based DDoS attack detection approach for classifying incoming IoT network traffic into 11 classes using multiclass machine learning techniques. The proposed approach comprises two phases: (i) designing and (ii) detection. In the designing phase, we employ the embedded feature reduction technique to create cost-effective and efficient classification models with a high feature reduction rate. Further, we evaluate these models using the K-fold cross-validation technique. While in the detection phase, we evaluate the performance of an efficient model by executing four different IoT traffic-based scenarios. A publicly available Bot-IoT dataset is employed to design and validate the proposed multiclass classification approach. The results show that the proposed approach provides an 84.4% feature reduction rate and approximately 5.19% higher classification accuracy than the existing approaches. [ABSTRACT FROM AUTHOR]

The healthcare sector is continuously evolving, and it is anticipated that the next healthcare model would be information-focused. Cloud computing technology can help the sector handle heterogeneous sources of data and the effective convergence of data. Due to security and privacy considerations, it is challenging to transfer a medical record from multiple hospital databases. Consequently, the article proposed a blockchain-based patient information management system by using the Blockchain-based Privacy-Preserving and Robust Healthcare data (BPPRH). This transfers the medical data to the patient through the cloud more securely. The cloud is modelled with the hypervisor and VMs. During the data transmission or storage in the cloud, there are several attacks like ransomware, malicious insiders, Man-in-the-middle attack, malware injection attacks, and Denial-of-Service attacks (DoS) occur in the network. Modified Fuzzy Particle Swarm Optimization (MFPSO) is presented to detect these threats. After detecting the attacks, Edge-Cloud-based Collaborative Systems (ECCS) prevent the data from attackers with the use of Regularized Maximum Likelihood Estimation, Inverse Network, and Shadow Model Reconstruction models. The results showed that the system provides better security with high accuracy of 99.32% respectively. The proposed Blockchain-based architecture is designed to contribute to the healthcare management systems' robustness and to avoid recorded security limitations in commonly used systems for smart healthcare respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel detection and rerouting mechanism for distributed adaptive platoon control of non‐linear autonomous connected vehicles under denial of service (DoS) attacks. DoS attacks can cause delays or losses of data packets due to blocked communication channels, leading to reducing platoon performance or even collisions among vehicles. To tackle this issue, the proposed mechanism detects and reroutes communication topology depending on the real‐time topology and the number of link failures. Real‐time detection divides the scenario of DoS attacks into three parts. According to the different scenarios, rerouting mechanisms will be utilized. A controller adapted to real‐time variable communication topology is also designed in this scheme. The adjacency matrix of the real‐time communication topology generated by the rerouting mechanism is used to update the controller so that the platoon can remain in a stable state without being affected by DoS attacks. In addition, the sliding mode controller and the observer are designed by solving linear matrix inequalities, and the platoon stability and internal stability are proven. Numerical simulation studies demonstrate that the proposed mechanism and control design can reduce the vehicle state estimate error and platoon‐tracking error to ideal states under DoS attacks. The proposed method solves the problem that the existing methods have not considered the number of link failures and the inability to restore communication when the communication topology is paralyzed. [ABSTRACT FROM AUTHOR]

The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches. [ABSTRACT FROM AUTHOR]

A serious threat to filter design is the difficulty in resisting important‐data‐based (IDB) Denial of Service (DoS) attacks. In this article, a new approach to filter design that is more relaxed and flexible in resisting IDB DoS policies is proposed. The proposed approach introduces the technique of homogeneous polynomials to develop a more general relaxed fuzzy filter design that can reduce conservatism. Moreover, it is guaranteed that the new method will obtain sets of more relaxed filter gains, which are more capable of responding to the IDB DoS policy. This enables the filter to perform more accurate signal estimation and ensures the filtering error system's H∞$$ {H}_{\infty } $$ performance. Finally, the reliability and effectiveness of the proposed filter method are demonstrated by two comparative examples. [ABSTRACT FROM AUTHOR]

This article is concerned with the issue of observer‐based event‐triggered H∞$$ {H}_{\infty } $$ control for networked switched systems (NSSs) under hybrid attacks. Since hybrid attacks can cause more damage to the system than a single cyber attack, this article considers a situation where stochastic deception attacks and aperiodic denial‐of‐service (DoS) attacks coexist in NSSs. To address this issue, the dynamics of the initial switched system combined with the event‐triggered mechanism (ETM), observers and hybrid attacks are formulated as a new augmented switched system. Thereinto, the unmeasurable states are estimated by a newly constructed gain adjustable observer and an observer state‐based adaptive ETM is designed under DoS attacks to save limited communication resources. Then, employing the Lyapunov stability theory and average dwell time (ADT) approach, sufficient conditions are obtained to guarantee the mean‐square exponential stability with a H∞$$ {H}_{\infty } $$ performance index of the new model. And the quantitative relationship between the switching signal and DoS attacks is formulated. Moreover, a co‐design method of ETM parameters and observer‐based controller gains is proposed. Compared with some existent results, restrictions on DoS attack signals and switching signals are relaxed. Finally, a numerical example is utilized to verify the feasibility and effectiveness of the proposed theory. [ABSTRACT FROM AUTHOR]

Denial of Service (DoS) attacks target the availability part of the CIA triad (Confidentiality, Availability, and Integrity). A special category of these attacks is the Distributed DoS (DDoS) attack, where the attacker uses a network of compromised systems called a botnet to flood a target server with requests and refuses to serve legitimate users. DDoS attacks can cost an organization millions of dollars in terms of lost revenue, remediation costs, and damage to brand reputation. Hence, all organizations need speedy real-time detection of DDoS attacks. This work presents a DDoS detection and classification framework using the flow-based approach for feature engineering and the AutoML technique. Our detection system is trained on the latest DDoS datasets – CIC-DDoS 2019 and CIC-IDS 2017, which contain various categories of DDoS attacks. We use various tools to perform adversarial attacks on our trained model. We retrain our models using adversarially crafted network packet captures and then test our models for robustness against practical adversarial attacks that an attacker might use to evade detection. Finally, we deploy our model in real-time using a GUI-based tool. Our model achieves a validation accuracy of 99.9% and a low false positive rate of 0.05%. [ABSTRACT FROM AUTHOR]

This paper investigates the secure optimal tracking control problem for cyber-physical systems, in which the controller-actuator channel is jammed by denial-of-service attacks. Without leveraging model knowledge, learning-based tracking control algorithms are proposed by utilizing the measured input-output data. The impact of malicious attacks on tracking performance is discussed. More specifically, an augmented system consisted of the system model and the reference model is derived. Then the states of the augmented system are rewritten by utilizing the input, output, and reference trajectory, following which both the Bellman equation and algebraic Riccati equation are given. The conditions of existence and uniqueness of the solution to the algebraic Riccati equation are derived. Furthermore, both policy iteration and value iteration learning-based tracking control algorithms are provided for cyber-physical systems against denial-of-service attacks, and the convergence of the algorithms is also proved. Finally, a DC motor system and a numerical example are given to illustrate the effectiveness of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

For nonlinear cyber-physical systems (CPSs) with input saturation and denial-of-service (DoS) attacks, an adaptive secure control strategy is investigated in this paper. To estimate the unmeasurable states, a novel switched neural network (NN) observer is designed, where two sub-observers can be freely switched from each other. Considering unnecessary packet transmissions, an improved event-triggered mechanism (ETM) is introduced to reduce the communication burden in the controller-to-actuator channel. In addition, in order to simultaneously solve the input saturation and unknown control direction problems, a Nussbaum-type function is introduced to the control design process. Finally, it is proven that all closed-loop signals are the semiglobal uniformly ultimately bounded, and simulation results demonstrate the effectiveness of the proposed secure control scheme. [ABSTRACT FROM AUTHOR]

Due to the close interaction between computing, sensing, communication, and driving of the cyber‐physical system (CPS), which makes it highly vulnerable to cyber attacks, the security of CPS has attracted wide attention. In the article, we discuss the system identification problem of the finite impulse response system under sequence denial of service attacks. The identification algorithm of the unknown parameter is designed under binary observations, and its convergence performance is analyzed. The optimal attack strategy problem is modeled as a optimized problem with constraints based on the covariance matrix of estimation error, the solution method and implementation algorithm are given. From the perspective of the defender, an encryption‐type defense algorithm is designed, and then the optimal defense strategy is presented. The numerical simulation is used to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the state estimation for cyber‐physical systems with multiple channels under joint Denial‐of‐Service and deception attacks. We show that the optimal estimator (OE) cannot be implemented in practice, as its computational complexity exponentially grows with respect to time. Then, we develop a centralized approximate optimal estimator (AOE) by using a generalized pseudo‐Bayesian algorithm. This AOE is not only computationally efficient, but also features theoretically guaranteed performance and stability. Specifically, it is proved when the attack success rate is less than a threshold value, both the OE and the AOE are stable, and the averaged performance deviation between the OE and the proposed AOE remains bounded. Finally, simulation examples are provided to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

This paper presents a prescribed performance‐based model‐free adaptive resilient control (MFARC) algorithm to address the realization of trajectory tracking for nonlinear cyber‐physical systems (CPSs) under aperiodic denial‐of‐service (DoS) attacks. Firstly, an equivalent linear model is constructed by utilizing the dynamic linearization technique, and a novel output transformation approach is applied to convert the constrained system to an unconstrained one for the MFARC framework. Then, an attack compensation mechanism is presented to estimate the unavailable output signal when attacks are active, such that the negative impact of aperiodic DoS attacks can be compensated. Based on the unconstrained model and the compensation mechanism, a prescribed performance‐based MFARC strategy is constructed to steer the tracking error to the predetermined neighborhood around the origin in the presence of DoS attacks. A major improvement of our result over previous studies is the introduction of prescribed performance control (PPC) into MFARC while reasonably restricting the transient and steady‐state performance of the tracking error even when DoS attacks occur. Additionally, we utilize the linear matrix inequality (LMI) toolbox to obtain better tracking performance by suitably adjusting the time‐varying parameters, which is superior in practical applications. Ultimately, our results indicate that the tracking error consistently remains within a predefined bound. [ABSTRACT FROM AUTHOR]

This paper proposes a data‐driven bipartite leader‐following consensus strategy for a class of nonlinear multi‐agent systems (MASs) under external disturbances and hybrid attacks, which are composed of denial‐of‐service attacks and false data injection attacks. This data‐driven algorithm incorporates no system dynamics and only utilizes the input and output data generated by the controlled plant. First, the nonlinear MAS with external disturbances can be transformed into an equivalent linear data model by applying a revised dynamic linearization method. Second, a hybrid‐attack compensation mechanism is proposed to alleviate the adverse impact of data dropout caused by hybrid attacks. Then, based on the compensation mechanism, an extended state observer is designed that can mitigate the negative influence induced by external disturbances and improve the control performance even though the MAS is threatened by hybrid attacks. The systems under hybrid attacks and external disturbances can still remain stable with the proposed data‐driven strategy. Finally, simulation examples demonstrate the validity of the data‐driven strategy, and the bipartite consensus error can be reduced to a small range. [ABSTRACT FROM AUTHOR]

A distributed control scheme based on historical information is designed to solve the problem of stable control of multi‐agent systems under denial of service (DoS) attacks in this article. It achieves the control objective of bipartite output containment control, that is, the output states of the followers smoothly enter the target area. The control scheme updates the states of followers through historical information in the control protocol when agents are subjected to DoS attacks. A distributed state observer with a storage module is designed to efficiently estimate the state of followers and store the observed information as history information. The historical information of control protocol calls is not necessarily the real state information in the existence of DoS attacks. Consequently, a closed‐loop feedback state compensator is designed. Then, the state compensator is converted from the time domain to the frequency domain for stability analysis using the Nyquist criterion. It is obtained that an upper bound on the amount of historical information can achieve the bipartite output trajectories containment of the controlled system. The output trajectories of the followers converge into two dynamic convex hulls, one of which is surrounded by multiple leaders, and the other is a convex hull with opposite signs of the leaders. Finally, a numerical simulation is used to verify the proposed control scheme, and the operability of the scheme is further demonstrated in a physical experiment. [ABSTRACT FROM AUTHOR]

Periodic event‐triggered control (PETC) can save network bandwidth and sensor energy at the same time; therefore, it is a promising approach for applications, especially large scale plants, such as smart water distribution systems. This work extend PETC by considering denial of service (DoS) attacks. For the PETC, a dynamic variable is introduced with a switched strategy. Different from other dynamic variables, our variable keeps store energy when there is no attacks and only release energy when attacks happen. Thus, the inter‐event intervals when attacks happen can be prolonged while guarantees the same stability and performance. If the variable equals to 0 before the ending of the attacks, then characteristic of the attack duration is analyzed to guarantee a slower Lyapunov convergence rate. The presented approach is verified via a numerical example from smart water distribution systems. [ABSTRACT FROM AUTHOR]