Multiple Cyber Attacks Research Articles

Residual Dense Optimization-Based Multi-Attention Transformer to Detect Network Intrusion against Cyber Attacks

Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model’s performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively.

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Two-channel dynamic event-triggered H∞ control for Markovian jump systems under multiple cyber-attacks

Two-channel dynamic event-triggered H∞ control for Markovian jump systems under multiple cyber-attacks

Dynamic event‐triggered H∞ control for switched T‐S fuzzy systems under multiple cyber attacks

Dynamic event‐triggered <i>H_∞</i> control for switched T‐S fuzzy systems under multiple cyber attacks

Stabilization of Periodic Piecewise Time-Varying Systems With Time-Varying Delay Under Multiple Cyber Attacks: An Augmented Lyapunov Functional Approach.

This article investigates the asymptotic stabilization of periodic piecewise time-varying systems with time-varying delay under various cyber attacks, particularly deception and acrlong DoS attacks. The addressed system is reformed into a number of time-varying subsystems based on the time interval for each period. Following that, a state-feedback controller with periodic time-varying gain parameters is developed to solve the stabilization problem. The control design depicts the possibility of the aforementioned cyber attacks with two mutually exclusive stochastic Bernoulli distributed parameters. Then, an augmented Lyapunov-Krasovskii functional with periodically varying matrices is used to determine the conditions for designing the proposed controller that ensures the mean-square asymptotic stability of the addressed system. The results of numerical examples support the conclusion that the proposed method is effective and superior, regardless of the cyber attacks involved.

Fully Distributed Hierarchical ET Intrusion- and Fault-Tolerant Group Control for MASs With Application to Robotic Manipulators

This paper studies group synchronization tracking problem for a class of high-order multi-agent systems (MASs) with nonidentical and unknown direction faults (NUDFs) under multiple cyber attacks (i.e., denial-of-service (DoS) attacks and false data injection attacks (FDIAs)). Be motivated by this, a fully distributed hierarchical (cyber layer and physical layer) Zeno-free event-triggered (ET) intrusion-and fault-tolerant controller is presented based on Nussbaum-type gain technique, where a positive inter-event time exists in the state-dependent asynchronous ET mechanism (ETM). The constructed virtual cyber layer realizes the interaction among different agents so as to avoid the interaction in physical processes and thus reduce the error propagation. This two layer controller greatly increase the flexibility of the controller design compared with single-layer control strategies. In addition, a novel Nussbaum function stability lemma (Lemma lem4lem4) is developed for the first time. Based on this lemma, the fully distributed adaptive controller can adjust the direction of the input to match the fault direction with the help of Nussbaum function. Finally, a robotic manipulator example demonstrates the effectiveness and merit of the proposed control scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In industrial processes, NUDFs and cyber attacks often occur in many different systems, which usually lead to performance degradation or even serious accidents. Typically, NUDFs affect the performance of chemical and industrial processes, circuits, and sensors. Meanwhile, in driverless vehicle platoon, attackers change the control signal through the wireless network, and then issue emergency braking commands, etc. Therefore, this paper designs a fully distributed hierarchical ET intrusion-and fault-tolerant scheme for high-order MASs that are usually used to model ship dynamics, robotic manipulators, and quarter-car active suspension. The control scheme gives solutions to the problem of NUDFs, multiple cyber attacks, and network bandwidth limitations at different layers, and effectively integrates the physical and cyber layers to achieve group synchronization. Meanwhile, a new event-triggered mechanism under the framework of group synchronization is developed to save communication resources. Robotic manipulator simulation studies verify the validity of the proposed scheme.

Dynamic Event-Triggered Control for GSES of Memristive Neural Networks Under Multiple Cyber-Attacks.

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

Observer-Based Dynamic Event-Triggered Control for CPSs Under Multiple Cyber-Attacks

Observer-Based Dynamic Event-Triggered Control for CPSs Under Multiple Cyber-Attacks

Energy-Management Strategy of Battery Energy Storage Systems in DC Microgrids: A Distributed Fuzzy Output Consensus Control Considering Multiple Cyber Attacks

Distributed renewable sources are one of the most promising contributors for DC microgrids to reduce carbon emission and fuel consumption. Although the battery energy storage system (BESS) is widely applied to compensate the power imbalance between distributed generators (DGs) and loads, the impacts of disturbances, DGs, constant power loads (CPLs) and cyber attacks on this system are not simultaneously considered. Based on this, a distributed fuzzy output consensus control strategy is proposed to realize accurate current sharing and operate normally in the presence of denial of service (DoS) attacks and false data injection (FDI) attacks. Firstly, the whole model of the BESS in DC microgrids embedded into disturbance items, DGs, CPLs and resistive loads, is firstly built. This model could be further transformed into standard linear heterogeneous multi-agent systems with disturbance, which lays the foundation for the following control strategy. Then the model of FDI and DoS attacks are built. Meanwhile, the fuzzy logic controller (FLC) is applied to reduce the burden of communication among batteries. Based on these, a distributed output consensus fuzzy control is proposed to realize accurate current sharing among batteries. Moreover, the system under the proposed control in different cases is analyzed. Finally, the feasibility of the proposed control strategy is verified by numerical simulation results and experiment results.

Coordinated control and energy management combined with cyberattack identification in multi‐microgrid integrated with hybrid renewable‐storage

AbstractA comprehensive model is developed for coordinated control of voltage‐frequency‐inertia and identifying multiple cyberattacks simultaneously in two microgrids (MGs). The MGs are integrated with solar units, Wind turbine (WT), hybrid supercapacitor‐battery, and fuelcell. The MGs are modelled and controlled for operation under both an island and connected states. In the proposed method, a data centre is designed in which all the electrical and control signals related to the solar, wind, hybrid supercapacitor‐battery, and Fuel cell (FC) are collected, evaluated, and matched. The data centre comprises the following blocks: voltage‐frequency control, inertia control of WT, and identification of false data injection (FDI) cyberattacks on frequency, power, power/frequency, and voltage. The technique used in this article to identify FDI attacks is based on the real‐time method coupled with logical comparisons conducted in the time domain. This methodology provides prompt and precise detection, allowing for timely preventive measures and strategic responses. After FDI attacks occur, the implemented control system effectively manages and regulates the voltage and frequency at the desired levels, efficiently differentiating between ordinary functioning, faulty states, and potential cyber‐attacks. The unhealthy MG can transfer its load to the healthy MG for safety reasons. The healthy MG is then connected to the external grid and the synchronisation conditions are checked by the proposed control system. The results of the non‐linear simulation performed in MATLAB‐Simulink software confirm that the proposed model successfully operates and controls all resources (i.e. solar/wind/battery/FC), regulates the voltage/frequency under various loading conditions, and identifies FDI cyberattacks.

Privacy-Preserving Adaptive Resilient Consensus for Multiagent Systems Under Cyberattacks

This article investigates the secure and privacy-preserving consensus problem of multi-agent systems (MASs) with directed interaction topologies under multiple cyber attacks, which contain deception attacks and DoS attacks. First, a unified attack model is introduced to characterize such a multiple attack phenomenon. Besides, considering the existence of eavesdroppers who can intercept the data transmitted on the links, a fully distributed agent value reconstruction method based on the idea of state decomposition is designed to prevent the leakage of the agent's initial information. Then, a novel privacy-preserving adaptive resilient consensus algorithm (PPARCA) with certain graph robustness condition for MASs under the multiple cyber attacks is proposed. The algorithm adaptively takes different countermeasures in the face of different cyber attacks. PPARCA uses the reconstructed agents' states and combines with the modified Secure Acceptance and Broadcast Algorithm (SABA). Theoretical analysis shows that the proposed algorithm can effectively protect the privacy of the initial state of the agents, and reach resilient consensus in the face of cyber attacks. Finally, numerical simulations and Raspberry Pi MASs practical application experiments demonstrate the effectiveness of the proposed results.

Dynamic event-based output feedback tracking control of nonlinear CPSs with cyber attacks

This paper discusses the problem of tracking control for nonlinear CPSs in the presence of resource constraints and multiple cyber-attacks. Firstly, a fuzzy model is built to solve the nonlinear characteristics of CPSs. Secondly, to reduce the burden on communication-resources, two dynamic event-triggered schemes are put forward from plant and reference. Considering the existence of cyber-attacks in dual channel, the deception-attacks model and DoS-attacks model are constructed by stochastic variables. This paper aims to obtain a dynamic event-based tracking controller under cyber-attacks. A sufficient condition of nonlinear CPSs is received to ensure the stochastic stability and desired tracking-performance. Finally, examples are simulated to testify the advantage of the proposed method.

Static output feedback quantized dissipative security control of singular hybrid systems subject to multiple cyber attacks: A dynamic-memory event-triggered strategy

This paper researches the problem of dynamic-memory event-triggered quantized dissipative security control for singular Markovian jump systems (SMJSs) subject to multiple cyber attacks, which comprise denial of service (DoS) attacks and multichannel deception attacks. By employing a set of historically released packets, a dynamic-memory event-triggered strategy is developed subject to DoS attacks which can decrease the occupancy rate of network communication bandwidth and achieve desired dissipative control performance. Furthermore, the closed-loop SMJSs are established by a switched system model on account of the simultaneous appearance of DoS attacks and deception attacks By constructing an improved mode-dependent piecewise Lyapunov–Krasovskii functional and applying singular value decomposition technique, the criteria of exponential admissibility and strict dissipativity for the controlled SMJSs are acquired by right of linear matrix inequalities. The idiographic form of the static output feedback quantized controller is expressed via a valid decoupling method. Finally, the validity of the proposed approach is testified by a numerical example and direct current motor driven load.

Enhanced lightweight and secure certificateless authentication scheme (ELWSCAS) for Internet of Things environment

The Internet of Things (IoT) is a fast-growing technology that enable existing systems to communicate with one another by using new devices such as sensors and other smart devices. The IoT devices such as wireless sensors and other smart devices are often very cheap, very small, and have limited power sources, memories, and processing capabilities. These devices are used in numerous critical applications, in which confidential information is sent across wireless channels among other devices participating in the system. This communication is vulnerable to multiple cyber-attacks due to the insecure wireless channel between them and without strong security mechanism, the important credential can be stolen by network attackers. One of the most important aspects of securing IoT communication is the authentication mechanism, used to validate the identity of authorized devices and users in IoT networks and to ensure data confidentiality, integrity, and authenticity for secure communication. Several authentication schemes for IoT environment have been proposed in the literature, none of these approaches entirely fulfil the necessary security and lightweight feature requirements. Some schemes fulfill the required security features but are unable to provide lightweight features. Similarly, other schemes provide lightweight features, but their security features are unsatisfactory. Therefore, it is necessary to design an effective security mechanism to ensure secure communication in IoT environments. In this paper, we propose Authenticated Encryption with Associated Data (AEAD) and Elliptic Curve Cryptography (ECC) based Enhanced Lightweight and Secure Certificateless Authentication Scheme (ELWSCAS) for IoT environment to fulfill the required security and lightweight performance features. The security of the proposed solution is evaluated using formal and informal security analysis. For formal security analysis we used RoR model and AVISPA tool. We have implemented the proposed solution in network simulator (NS3.35) by using Python and C++ to measure the network parameters such as throughput and packet delivery ratio (PDR). The computational and communication cost of the proposed scheme shows that in comparison to the existing state-of-the-art, our approach is considerably less costly and is a viable option for constrained IoT environment.

Security-Based Control for Networked Interval Type-2 Fuzzy Systems With Multiple Cyber-Attacks: An Improved Dynamic Event-Triggered Scheme

This paper addresses the problem of dynamic event-triggered (DET) control for networked interval type-2 (IT2) fuzzy systems subject to multiple cyber-attacks, which contain deception attacks and aperiodic denial-of-service attacks simultaneously. Firstly, a multiple cyber-attack model is established for the IT2 fuzzy systems with multiple cyber-attacks and external disturbances. Secondly, an improved DET mechanism (DETM) is proposed to reduce the communication burden. Contrasting with existing works, by introducing two adjustable parameters into the novel DETM, the presented DETM has more flexibility in adjusting the data packet transmissions to conserve more limited network resources. Meanwhile, the Zeno behavior is probably excluded under the proposed DETM while preserving the system performance. Thirdly, imperfectly matched membership functions (MFs) are considered between fuzzy controller and IT2 fuzzy model, and the global boundary information of MFs and slack matrices are introduced to relax stability conditions. Besides, by constructing a proper Lyapunov-Krasovskii functional, sufficient conditions are obtained to ensure the exponentially mean-square stable of the IT2 fuzzy systems with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results.

Dynamic Event-Triggered H ∞ Filtering for NCSs Under Multiple Cyber-Attacks

This article considers the dynamic event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> filtering problem for networked control systems (NCSs) under multiple cyber-attacks. By taking into account both denial-of-service (DoS) and deception attacks (DAs), a novel dynamic event-triggering mechanism (DETM) and the corresponding event-triggered filter are presented. The proposed DETM further reduces data transmissions and also guarantees the exclusion of Zeno behavior. By resorting to a piecewise Lyapunov functional (PLF), sufficient conditions are derived to ensure that the resulting filtering error system is globally stochastically exponentially stable with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance in the presence of both DoS and DAs. A simulation example of a tunnel diode circuit system is displayed to illustrate the validity of the proposed event-triggered filter under multiple cyber-attacks.

Identification and classification for multiple cyber attacks in power grids based on the deep capsule CNN

Cyber-attacks have become one of the main threats to the security, reliability, and economic operation of power systems. Detection and classification of multiple cyber-attacks pose challenges for ensuring the stability and security of power systems. To address this issue, this study proposes an automatic identification and classification method for multiple cyber-attacks based on the deep capsule convolution neural network. Spatial correlations among different nodes and temporal features from history operation status in the transmitted data packets are extracted by the convolution neural network. Capsules in the proposed structure have important implications for maintaining the topological consistency contained in the measurement matrix. Furthermore, the proposed method is model-free and avoids the impact of system parameters uncertainties on detection performance. Multiple kinds of typical cyber-attacks, including false data injection attacks, replay attacks, denial of service attacks, time-delay attacks, and deception attacks, are considered and modeled in this paper. Numerical results on the IEEE 39-bus test system show that the proposed method can achieve 99.97% detection accuracy on single cyber-attacks and 96.25% detection accuracy on multiple cyber-attacks. Comparative results illustrate the proposed method outperforms than traditional neural networks. This approach provides a solution for the problem of multiple cyber-attack detection and classification.

Non-fragile guaranteed cost control for networked nonlinear Markov jump systems under multiple cyber-attacks

This paper is concerned with the problem of non-fragile guaranteed cost control (GCC) for networked nonlinear Markov jump systems subject to multiple cyber-attacks, which are characterized by Takagi–Sugeno (T–S) fuzzy model with time-varying delay. Specifically, a variety of cyber-attacks, including deception attacks and Denial-of-Service (DoS) attacks, are considered, which occur in the forward and feedback communication links, respectively. To achieve stochastic stability under guaranteed cost function (GCF), the paper proposes a Lyapunov–Krasovskii (L–K) function approach. The approach derives sufficient conditions for stochastic stability, and obtains non-fragile controller gains and the uniform upper bound of the GCF using linear matrix inequalities (LMIs) technique. Finally, the effectiveness of the proposed algorithm is evaluated by simulation experiment.

Edge-event-triggered encryption-decryption observer-based control of multiagent systems for privacy protection under multiple cyber attacks

This study investigates the secure and privacy-preserving consensus problem for a class of Lipschitz nonlinear multiagent systems (MASs) under undirected topology and multiple cyber attacks. An observer-based dynamic cryptography-based encryption-decryption (CED) pairing algorithm is proposed to achieve privacy preservation and defend against replay attacks simultaneously. To reduce the computation and communication resources and facilitate paired password design, a Zeno-free edge-event-triggered mechanism (EETM) based on edge (rather than neighbor) information is developed in a completely distributed manner, using only local observation information without any knowledge about the global topology. The designed EETM can accommodate multiple event-triggered functions, which simplifies the EETM design procedure. Interestingly, the CED pairing algorithm makes the effect of replay attacks equivalent to that of denial-of-service (DoS) attacks. Thus, DoS and replay attacks can be handled as the same types of attacks. In addition, a resilient EETM-based control strategy against DoS and replay attacks is proposed to make the MAS reach consensus faster, within the attack sleep time. Furthermore, sufficient conditions for the frequency and duration of replay and DoS attacks are established to achieve secure consensus of the MAS. Finally, a numerical example is provided to demonstrate the validity and superiority of the proposed method.

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach.

Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient-monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph-theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyberrisk.