Denial Of Service Attacks Research Articles

A quantization-coding scheme with variable data rates for cyber-physical systems under DoS attacks

This paper considers Cyber-Physical Systems (CPSs) in the presence of Denial-of-Service (DoS) attacks, and investigates the networked control problem under data rate limitations. We develop a framework for CPSs in the presence of DoS attacks, and present a state-estimate control scheme for CPSs under DoS attacks. For the different communication status, the information on the plant state is transmitted to the controller by employing a different data rate in order to mitigate the influence of DoS attacks. Sufficient conditions on the data rate for mean square observability and stabilization of CPSs are derived by applying information theoretic tools of source coding. Our result is less conservative than some existing results. An illustrative example is given to demonstrate the effectiveness of the proposed quantization-coding scheme.

Joint non-fragile automatic generation control and multi-event driven mechanism co-design for wind integrated power system under denial of services attacks

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism.

Attack-Resilient Distributed Nash Equilibrium Seeking of Uncertain Multiagent Systems Over Unreliable Communication Networks.

This article investigates the distributed Nash equilibrium (NE) seeking problem of uncertain multiagent systems in unreliable communication networks. In this problem, the action of each agent is subject to a class of nonlinear systems with uncertain dynamics, and the communication network among agents will be affected by the nonperiodic denial of service (DoS) attacks. Note that, in this insecure network environment, the existence of DoS attacks will directly destroy the connectivity of the network, which leads to performance degradation or even failure of the most existing distributed NE seeking algorithms. To address this problem, we propose a two-stage distributed NE seeking strategy, including the attack-resilient distributed NE estimator and the neuroadaptive tracking controller. The estimator based on the projection subgradient method and the consensus protocol can converge exponentially to virtual NE against DoS attacks. Then, the neuroadaptive tracking controller is designed for uncertain multiagent systems with the output of the estimator as the reference signal such that the actual action of all agents can reach NE. Based on the Lyapunov stability theory and improved average dwell time automaton, the stability of the estimator and the controller is proven, and all signals in the closed-loop system are uniformly bounded. Numerical examples are presented to verify the effectiveness of the proposed strategy.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

Important-Data-Based DoS Attack Mechanism and Resilient H∞ Filter Design for Networked T-S Fuzzy Systems.

This article is concerned with the security problems for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most "important" ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient H∞ fuzzy filter is designed from the defender's point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T-S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the H∞ performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient H∞ filter.

Resilient Output Containment Control of Heterogeneous Multiagent Systems Against Composite Attacks: A Digital Twin Approach.

This article delves into the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including Denial-of-Service (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twin technology, a twin layer (TL) with higher security and privacy is employed to decouple the above problem into two tasks: 1) defense protocols against DoS attacks on TL and 2) defense protocols against actuation attacks on the cyber-physical layer (CPL). Initially, considering modeling errors of leader dynamics, distributed observers are introduced to reconstruct the leader dynamics for each follower on TL under DoS attacks. Subsequently, distributed estimators are utilized to estimate follower states based on the reconstructed leader dynamics on the TL. Then, decentralized solvers are designed to calculate the output regulator equations on CPL by using the reconstructed leader dynamics. Simultaneously, decentralized adaptive attack-resilient control schemes are proposed to resist unbounded actuation attacks on the CPL. Furthermore, the aforementioned control protocols are applied to demonstrate that the followers can achieve uniformly ultimately bounded (UUB) convergence, with the upper bound of the UUB convergence being explicitly determined. Finally, we present a simulation example and an experiment to show the effectiveness of the proposed control scheme.

Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss

This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions.

Representation-Learning-Based CNN for Intelligent Attack Localization and Recovery of Cyber-Physical Power Systems.

Enabled by the advances in communication networks, computational units, and control systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart systems in which a large amount of data are generated, exchanged, and processed for various purposes. Due to these strong interactions, CPPSs will introduce new security vulnerabilities. To ensure secure operation and control of CPPSs, it is essential to detect the locations of the attacked measurements and remove the state bias caused by malicious cyber-attacks such as false data inject attack, jamming attack, denial of service attack, or hybrid attack. Accordingly, this article makes the first contribution concerning the representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of CPPSs. In the proposed method, the cyber-attacks' locational detection problem is formulated as a multilabel classification problem for CPPSs. An RL-CNN is originally adopted as the multilabel classifier to explore and exploit the implicit information of measurements. By comparing with previous multilabel classifiers, the RL-CNN improves the performance of attack localization for complex CPPSs. Then, to automatically filter out the cyber-attacks for system recovery, a mean-squared estimator is used to handle the difficulty in state estimation with the removal of contaminated measurements. In this scheme, prior knowledge of the system state is obtained based on the outputs of the stochastic power flow or historical measurements. The extensive simulation results in three IEEE bus systems show that the proposed method is able to provide high accuracy for attack localization and perform automatic attack filtering for system recovery under various cyber-attacks.

Security dissipative control for interval type‐2 fuzzy hidden Markovian jump systems under event‐triggered scheme

AbstractThis article presents a new design approach for resilient fault‐tolerant event‐triggered (ET) based security control of Markovian jump nonlinear systems (MJNSs) in the presence of nonperiodic denial‐of‐service (DoS) attacks. The approach is developed based on interval type‐2 (IT‐2) fuzzy models of the plant. The state error produced by data packet loss is turned into a parameter in the proposed ET condition during nonperiodic DoS attacks. The uncertain triggering variable is considered in the resilient fault‐tolerant ET condition for the MJNSs. In contrast to the usual strategy, the fault‐tolerant ET strategy is designed to send the required data packets to the controller during nonperiodic DoS attacks, which significantly contributes to the minimization of performance loss. Dissipative, passive, and ‐control schemes are all covered in a unifying framework by using a generalized performance index. Plant and controller modes are considered under mismatched membership functions to regulate asynchronous phenomena. Sufficient conditions depending on the membership functions that guarantee the stability of the closed‐loop system are derived. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed approach.

Cluster-based wireless sensor network framework for denial-of-service attack detection based on variable selection ensemble machine learning algorithms

A Cluster-Based Wireless Sensor Network (CBWSN) is a system designed to remotely control and monitor specific events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. The wide range of applications of technology in almost every field of human activity exposes it to various security threats from cybercriminals. One of the pressing concerns that requires immediate attention is the risk of security breaches, such as intrusions in wireless sensor network traffic. Poor detection of denial-of-service (DoS) attacks, such as Grayhole, Blackhole, Flooding, and Scheduling attacks, can deplete the energy of sensor nodes. This can cause certain sensor nodes to fail, leading to a degradation in network coverage or lifetime. The detection of such attacks has resulted in significant computational complexity in the related works. As new threats arise, security attacks get more sophisticated, focusing on the target system's vulnerabilities. This paper proposed the development of Cluster-Based Wireless Sensor Network and Variable Selection Ensemble Machine Learning Algorithms (CBWSN_VSEMLA) as a security threats detection system framework for DoS attack detection. The CBWSN model is designed using a Fuzzy C-Means (FCM) clustering technique, whereas VSEMLA is a detection system comprised of Principal Component Analysis (PCA) for feature selection and various ensemble machine learning algorithms (Bagging, LogitBoost, and RandomForest) for the detection of grayhole attacks, blackhole attacks, flooding attacks, and scheduling attacks. The experimental results of the model performance and complexity comparison for DoS attack evaluation using the WSN-DS dataset show that the PCA_RandomForest IDS model outperforms with 99.999 % accuracy, followed by the PCA_Bagging IDS model with 99.78 % accuracy and the PCA_LogitBoost model with 98.88 % accuracy. However, the PCA_RandomForest model has a high computational complexity, taking 231.64 s to train, followed by the PCA_LogitBoost model, which takes 57.44 s to train, and the PCA_Bagging model, which takes 0.91 s to train to be the best in terms of model computational complexity. Thus, the models surpassed all baseline models in terms of model detection accuracy on flooding, scheduling, grayhole, and blackhole attacks.

Event‐triggered secure control under denial‐of‐service attacks for cyber‐physical systems with positive constraint

AbstractThis paper investigates the stabilization problem for cyber‐physical systems subject to positive constraint and denial‐of‐service (DoS) attacks using the event‐triggered scheme. Unlike traditional modeling of cyber physical systems with DoS attack, this paper establishes a positive switched system model composed of two positive subsystems that according to the DoS attack is active or inactive. Considering the effect of positive constraint and DoS attack, a 1‐norm‐based event‐triggered mechanism is proposed in the sleep interval of DoS attacks. By incorporating switching theory with event‐triggered mechanism, the sufficient condition of positive property and exponential stabilization for the considered system is established. Thereafter, the gain matrix of the controller is calculated by matrix decomposition technique. Furthermore, the Zeno behavior is eliminated with a lower bound of the event‐triggered interval. Finally, an example is carried out to verify the validity of the theoretical results.

Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model

Abstract Network security faces increasing threats from denial of service (DoS) and distributed denial of service (DDoS) attacks. The current solutions have not been able to predict and mitigate these threats with enough accuracy. A novel and effective solution for predicting DoS and DDoS attacks in network security scenarios is presented in this work by employing an effective model, called CNN-LSTM-XGBoost, which is an innovative hybrid approach designed for intrusion detection in network security. The system is applied and analyzed to three datasets: CICIDS-001, CIC-IDS2017, and CIC-IDS2018. We preprocess the data by removing null and duplicate data, handling imbalanced data, and selecting the most relevant features using correlation-based feature selection. The system is evaluated using accuracy, precision, F1 score, and recall. The system achieves a higher accuracy of 98.3% for CICIDS-001, 99.2% for CICIDS2017, and 99.3% for CIC-ID2018, compared to other existing algorithms. The system also reduces the overfitting of the model using the most important features. This study shows that the proposed system is an effective and efficient solution for network attack detection and classification.

Fixed‐time consensus of networked Euler–Lagrange systems over event‐based communication under DoS attacks

AbstractThis article focuses on fixed‐time consensus of networked Euler–Lagrange systems (NELSs) over event‐based communication under denial‐of‐service (DoS) attacks. First of all, a basic fixed‐time consensus algorithm is developed for NELSs, where a general fixed‐time communication triggering scheme through error decomposition method is designed for decreasing communication frequencies while avoiding singularity. Then, to fulfil the consensus problem of NELSs in the presence of DoS attacks, a novel fixed‐time resilient algorithm is further presented by the basic algorithm, where the effective attack interval is redefined for carrying out the algorithm design. Furthermore, a hierarchical fixed‐time control framework with privacy preservation is introduced for the consensus problem of NELSs under DoS attacks. Meanwhile, a self‐triggered scheme is employed for the above algorithms to lower the update frequencies of control inputs. The stated algorithms possess a uniformly constrained upper bound on the convergence time, and are resilient as well as responsive to DOS attacks while saving control resources significantly. Finally, numerous illustrative examples are given for demonstrating the efficacy of the derived schemes.

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

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).

Co-design of distributed dynamic event-triggered scheme and extended dissipative consensus control for singular Markov jumping multi-agent systems under periodic Denial-of-Service jamming attacks

This paper introduces a distributed dynamic event-triggered (DDET) controller meticulously crafted to achieve resilient consensus control in singular Markov jumping multi-agent systems (SMJMAs) even in the face of periodic Denial-of-Service (DoS) jamming attacks. With a dual focus on optimizing network resource utilization and fortifying against cyber threats, we propose a distributed dynamic event-triggered scheme (DDETS) grounded in the principles of sampled data consensus. In the presence of cyber attacks, we demonstrate the transformation of the original consensus control challenge within the singular multi-agent system into a robust stability control problem, centering around a singular switching error system governed by the DoS attack signal. Acknowledging the disturbances that typify real-world scenarios, this paper establishes criteria for attaining exponential stability and stochastic admissibility within the singular Markov jumping switching error system under the extended dissipative performance index. Furthermore, we present a hybrid design for the consensus controller and DDETS, delineated through a set of solvable linear matrix inequalities (LMIs). Finally, a comprehensive array of numerical examples meticulously illustrates the compelling effectiveness and unequivocal superiority of our proposed methodology.

Fusion estimation against mixed network attacks for systems with random parameter matrices, correlated noises, and quantized measurements

This paper is concerned with the information fusion estimation problems for stochastic uncertain systems with quantized measurements and mixed network attacks including random deception attack and denial-of-service (DoS) attack. The cross-correlated random parameter matrices and addictive noises simultaneously exist in the studied system. By resorting to the optimal prediction compensation mechanism for DoS attack, an optimal centralized fusion filter in the linear minimum variance sense is proposed using an innovation analysis approach. In addition, the Kalman-like recursive distributed optimal linear fusion predictor and filter without feedback are presented based on local estimators from single-sensor subsystems. The estimation error cross-covariance matrices between two arbitrary local estimators, and those between local and prior fusion estimators are derived. They have good flexibility due to the parallel structure. However, they have lower accuracy than the centralized fusion estimators. To further improve the estimation accuracy, the distributed optimal linear fusion predictor and filter with feedback are also presented. They avoid the calculation of cross-covariance matrices. Moreover, it has been mathematically proved that they have the same estimation accuracy as the centralized fusion estimators. A simulation example demonstrates the effectiveness of the proposed algorithms.

Event-triggered predictive control for cooperation-competition multi-agent systems under DoS attacks

This paper concerns the bipartite consensus problem of multi-agent systems(MASs) with competitive- cooperative network topology under denial-of-service (DoS) attacks. Firstly, this work extensively analyzes the competitive phenomena that may exist in the information interchange of agents in contrast to the single cooperative behavior between agents. Based on this, some necessary conditions are provided for the system to attain the bipartite consensus. In addition, the event-triggered mechanism (ETM) effectively lowers unnecessary information sharing between agents and eliminates Zeno behavior. Furthermore, the predictive method provides the system with exceptional resistance against common energy-limited DoS attacks and the ability to compensate for information loss caused by DoS attacks. Finally, a numerical simulation proves that the proposed approach is feasible.

Building IPv6 addressing scheme using Hybrid Duplicate Address Detection to prevent Denial of Service Attack

Internet Protocol version 6 (IPv6) address configuration is needed to meet the needs of current portable devices and wearable applications, where each device needs its own unique identification number. High complexity in Wireless Sensor Network (WSN) and Internet of Things (IoT) environments has increased challenges, such as low energy consumption and high overhead in IPv6 address configuration. The proposed IPv6 generation is based on a hybrid mathematical approach that retains the IP address’s confidentiality and prevents Denial of Service (DoS) attacks from new and existing nodes in the network. In this paper, the proposed hybrid protocol for DoS attacks from new and existing nodes is analyzed. It is evident from the results that the rate of successful configuration, overhead, and energy consumption is found to be better than the existing approach, namely, Programming Protocol-independent Packet Processors Duplicate Address Detection (P4DAD), match prevention technique, etc.

Communication network robust routing optimization in an integrated energy cyber–physical system based on a random denial-of-service attack

The integration of power grids and communication networks in smart grids enhances system safety and reliability but also exposes vulnerabilities to network attacks, such as Denial-of-Service (DoS) attacks targeting communication networks. A multi-index evaluation approach is proposed to optimize routing modes in integrated energy cyber-physical systems (IECPS) considering potential failures from attacks. Security and economic service evaluation indexes are incorporated to quantify the significance of information flow routing. An optimization model for electric, heat, and gas routing in worst-case scenarios is formulated and solved using a column and constraint generation algorithm. The optimized routing method effectively circumvents specified attack areas, reducing the correlation degree of communication links within the attack area. Comparison with single-service optimization methods demonstrates the superiority of the proposed approach in mitigating the impact of network attacks on IECPS. The study highlights the importance of considering security and economic factors in optimizing routing modes to enhance the resilience of integrated energy cyber-physical systems against network attacks, particularly DoS attacks on communication networks. The evaluation index approach presented in this study provides a comprehensive method for assessing the importance of communication links in IECPS and optimizing routing modes to improve system robustness and reliability in the face of network attacks.