Denial Of Service Attacks Research Articles

Bipartite containment of heterogeneous multi‐agent systems under denial‐of‐service attacks: A historical information‐based control scheme

AbstractA 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.

Secured consensus control for multi-agent systems under DoS attacks: Coding–decoding communication protocol

This paper focuses on the leader following consensus control problem for multi-agent systems (MASs) subject to denial-of-service (DoS) attacks. First, a sliding mode observer is developed to estimate each agent state, actuator, and sensor fault. Then, a coding–decoding communication protocol is proposed to enhance the communication security between agents. Based on the sliding mode observer and communication protocol, a leader-following consensus controller is designed to guarantee the consensus and input-to-state stability of MASs under DoS attacks. Finally, the effectiveness of the developed controller is verified by a simulation example.

Inherent attack tolerance properties of model predictive control under DoS attacks

We consider the inherent attack tolerance properties of resilient model predictive control (R-MPC) for cyber–physical systems (CPSs) modeled by discrete linear time-invariant (LTI) systems subjected to limited disruptions. In this paper, the relationship between the maximum allowable duration of Denial-of-Service (DoS) attacks, namely attacks that disrupt both sensor to controller (S–C) and controller to actuator (C–A) communication channels, and the upper bound of disturbances is deduced at length. Moreover, to achieve robust recursive feasibility and closed-loop stability of the system, we discuss that MPC with state, control and terminal constraints has a certain degree of inherent attack tolerance concerning the DoS attack duration and parameters like system matrices and the nominal terminal set. Finally, numerical simulation is given to substantiate the effectiveness of the proposed approach.

The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks

In this paper, we investigate tracking control of heterogeneous multi-agent systems under Denial-of-Service (DoS) attacks and state quantization. Dynamic quantized mechanisms are designed for inter-follower communication and leader–follower communication. Zooming-in and out factors, and data rates of both mechanisms for preventing quantizer saturation are provided. Our results show that by tuning the inter-follower quantized controller, one cannot improve the resilience beyond a level determined by the data rate of leader–follower quantized communication, i.e., the ceiling effect. Otherwise, overflow of followers’ state quantizer can occur. On the other hand, if one selects a “large” data rate for leader–follower quantized communication, then the inter-follower quantized communication determines the resilience, and further increasing the data rate for leader–follower quantized communication cannot improve the resilience, i.e., the bottleneck effect. Simulation examples are provided to justify the results of our paper.

Machine learning based detection of DDoS attacks in software defined network

<span>Nowadays, software defined networking (SDN) offers benefits in the area so fautomation, elasticity, and resource consumption. However, evidenceis there that SDN controller may undergo certain defeat for the network structure, particularly as the yare targeted by attacks like denial of service (DoS). Due to this network traffic has increased tremendously and attacked the server severely. To handle this issue, weused the Ryu controller and Mininet tool to identify and all eviate the DoS attack by the machine learning (ML) algorithm. Since ML is deemed as themain method for detecting peculiarities, the detection of DoS attacks was done through ML based classification. In this paper, several ML techniques were used to identify the DoS attack, and the traffic which is causing the attack has been dropped immediately to avoid congestion. The proposed work hasbeen simulated in Mininet and the results show that the proposed work detects DoS attacks well and achieves good accuracy.</span>

Consensus Control of Multiagent Systems Under Switching Topology and Denial-of-Service Attacks

This article studies the consensus problem of multiagent systems (MASs) under denial-of-service (DoS) attacks and switching topology. Compared with the previous papers, under the switching topology, the impact of DoS attacks on the MAS is considered, and thus, the difficulty of consensus protocol design is further increased. First, a piecewise resilient consensus protocol is proposed to ensure that the MAS can achieve consensus under switching topology and DoS attacks. Then, by considering the different convergence rates caused by the switching topology in each sleep period of DoS attacks, a piecewise Lyapunov–Krasovskii function is established. The sufficient conditions on the global convergence of the system are obtained under switching topology and DoS attacks. In the meantime, the lower bound of average dwell time of the switching topology is determined under DoS attacks. The relationship between average dwell time and the parameters of DoS attacks is discussed. Finally, the simulation example is conducted to verify the effectiveness of the proposed method.

Observer-Based Adaptive NN Security Control for Switched Nonlinear Systems Against DoS Attacks: An ADT Approach.

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

Stability Analysis for Model-Based Event-Triggered Nonlinear Control Systems Under DoS Attacks

In this paper, we study the stability of a novel nonlinear event-triggered control system under denial-of-service (DoS) attacks based on an ISS-Lyapunov function analysis. A new dual-mode model-based event-triggering strategy is proposed which works based on consistency between the approximate and exact discretization of the nonlinear plant. We combine an event-triggered module with a controller based on the state prediction, together with a packetized transmission at the controller-to-actuator channel to provide the desired stability properties under DoS attacks. We also provide proof of Zeno-free behavior for the event-based system. Our proposed method provides a maximum percentage of time that the system can tolerate attacks without performance degradation. Finally, a numerical example is used to illustrate the effectiveness of the proposed approach.

Anomaly Detection for Modbus over TCP in Control Systems Using Entropy and Classification-Based Analysis

This article presents a statistical approach using entropy and classification-based analysis to detect anomalies in industrial control systems traffic. Several statistical techniques have been proposed to create baselines and measure deviation to detect intrusion in enterprise networks with a centralized intrusion detection approach in mind. Looking at traffic volume alone to find anomalous deviation may not be enough—it may result in increased false positives. The near real-time communication requirements, coupled with the lack of centralized infrastructure in operations technology and limited resources of the sensor motes, require an efficient anomaly detection system characterized by these limitations. This paper presents extended results from our previous work by presenting a detailed cluster-based entropy analysis on selected network traffic features. It further extends the analysis using a classification-based approach. Our detailed entropy analysis corroborates with our earlier findings that, although some degree of anomaly may be detected using univariate and bivariate entropy analysis for Denial of Service (DOS) and Man-in-the-Middle (MITM) attacks, not much information may be obtained for the initial reconnaissance, thus preventing early stages of attack detection in the Cyber Kill Chain. Our classification-based analysis shows that, overall, the classification results of the DOS attacks were much higher than the MITM attacks using two Modbus features in addition to the three TCP/IP features. In terms of classifiers, J48 and random forest had the best classification results and can be considered comparable. For the DOS attack, no resampling with the 60–40 (training/testing split) had the best results (average accuracy of 97.87%), but for the MITM attack, the 80–20 non-attack vs. attack data with the 75–25 split (average accuracy of 82.81%) had the best results.

Sajber fizička bezbednost distribuiranih energetskih resursa

Huge amounts of data are coming from the electrical grid, through smart meters, smart inverters, and Supervisory Control and Data Acquisition (SCADA) protocols. This makes emerging power systems, and especially distribution grids, heavily dependent on real-time data from the field, as well as on the IT infrastructure for observation and control of field devices. As a consequence, emerging electrical systems are becoming fully digitized, cyber-physical systems with all the accompanying advantages and challenges. Such a power system is called a “Smart Grid”. As all cyber-physical systems, Smart Grids are heavily dependent on Information and Communication Technology (ICT) infrastructure. Therefore, they are highly vulnerable to attacks that can compromise uninterrupted data flow, such as denial of service attacks and interruptions due to natural disasters, cataclysmic events, or wars, as well as due to malicious cyber-attacks. In this paper, we summarize the state-of-the-art in the cyberphysical security of distributed energy resources (DERs). We explore pain points to which monitoring and control of DERs in emerging electrical systems are exposed due to malicious cyberattacks. We continue with analyzing the accompanying consequences to the safety and reliability of emerging Smart Grids. Finally, we present our idea of Digital Twin technology as a tool for assisting distribution system operators and grid engineers, to detect, attenuate, and prevent malicious cyberattacks in DERs.

Optimized Adaptive Fuzzy Security Control of Nonlinear Systems With Prescribed Tracking Performance.

This article studies the optimized fuzzy prescribed performance control problem for nonlinear nonstrict-feedback systems under denial-of-service (DoS) attacks. A fuzzy estimator is delicately designed to model the immeasurable system states in the presence of DoS attacks. To achieve the preset tracking performance, a simper prescribed performance error transformation is constructed considering the characteristics of DoS attacks, which helps obtain a novel Hamilton-Jacobi-Bellman equation to derive the optimized prescribed performance controller. Furthermore, the fuzzy-logic system, combined with the reinforcement learning (RL) technique, is employed to approximate the unknown nonlinearity existing in the prescribed performance controller design process. An optimized adaptive fuzzy security control law is then proposed for the considered nonlinear nonstrict-feedback systems subject to DoS attacks. Through the Lyapunov stability analysis, the tracking error is proved to approach the predefined region by the preset finite time, even in the presence of DoS attacks. Meanwhile, the consumed control resources are minimized due to the RL-based optimized algorithm. Finally, an actual example with comparisons verifies the effectiveness of the proposed control algorithm.

Resilient Event-Triggered Control of Connected Automated Vehicles Under Cyber Attacks

Dear Editor, This letter contributes to designing a resilient event-triggered controller for connected automated vehicles under cyber attacks, including denial-of-service (DoS) and deception attacks. To characterize the effect of DoS attacks, the effective intervals of the attack are redivided based on the sampling period. Then, a resilient distributed event-triggering mechanism is proposed to compensate for the sabotage of DoS attacks and reduce the amount of transmitted data. Since the communication channel transmits the data only at the trigger instant, deception attacks may occur at this instant and be transmitted to each vehicle in superposition with the normal signal. Therefore, we construct stochastic models satisfying Bernoulli distribution to describe the false information injected by the attackers. Based on the above framework, an attack-resilient control strategy is proposed to resist the impact of cyber attacks. Then, sufficient conditions are established to achieve stability of vehicular platoons, and a co-design strategy regarding the control gain and triggering parameter matrices is given. Finally, the simulation results are provided to substantiate the effectiveness of the proposed method.

Estimator-based event-triggered output synchronization for heterogeneous multi-agent systems under denial-of-service attacks and actuator faults

This paper studies the output synchronization problem with an event-triggered communication scheme for a class of heterogeneous multi-agent systems under denial-of-service (DoS) attacks and actuator faults. DoS attacks may render the followers incapable of acquiring the leader's states and actuator faults may cause the actuators to not perform the control operation well. Comparing with the previous studies adopting the continuous communication mechanism, a novel switching estimator with an event-triggered communication strategy is designed to estimate the leader's states for each follower. By using the Lyapunov stability theory and the average dwell time method, sufficient conditions for the upper bound of the frequency of DoS attacks are derived. The estimation error system is proved to be exponentially stable. Besides, in order to compensate for actuator faults, a distributed adaptive fault-tolerant controller is proposed. The conditions for the stability of the output regulation error when the systems suffer from DoS attacks and actuator faults are given. A simulation example is proposed to prove the effectiveness of the main results.

Game learning‐based system identification with binary‐valued observations under DoS attacks

SummaryThe rapid progress in computer, communication, and sensor technology has led to the proliferation of cyber‐physical systems (CPSs) which have become integral to various sectors. However, their heavy dependence on open communication networks makes them vulnerable to network‐based attacks. To tackle these security concerns, this paper delves into game learning‐based system identification with binary‐valued observations in the presence of Denial‐of‐Service (DoS) attacks. We first formulate a game model to capture interactions between the attacker and defender. Focusing on piecewise constant DoS attacks, we then devise a defense strategy grounded in game learning principles. This strategy paves the way for crafting estimation algorithms for both the attack strategy and system parameters, with their performance scrutinized in specific stages. Through meticulous analysis and comprehensive numerical simulations, we have observed that the game learning approach outperforms the randomly selected defense strategy in terms of parameter estimation. This provides a novel and reliable approach to address security challenges within CPSs.

Optimal tracking performance of networked control systems under communication constraints

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output discrete-time communication-constrained systems by thinking about Denial of Service (DoS) attacks, codecs and additive Gaussian white noise under energy constraints. The non-cooperative relationship between DoS attacks and intrusion detection systems (IDS) is analyzed using repeated game theory. A penalty mechanism is constructed to force the attackers to adopt a cooperative strategy, thus improving the system performance. Partial factorization and spectrum decomposition are used to provide the OTP for systems. The results demonstrate that the systems’ OTP are linked to intrinsic characteristics like non-minimum phase zeros and unstable poles. Finally, concrete examples are shown that the results are accurate.

Security consensus control for multi-agent systems under DoS attacks via reinforcement learning method

This paper is concerned with the security consensus control issue for discrete-time multiagent systems (MASs) on the basis of a reinforcement learning (RL) approach. Considering the effects of denial-of-service (DoS) attacks, a novel control protocol is proposed to deal with the H∞ consensus problem. Firstly, a Q-learning algorithm is put forward under the directed graph, which can obtain the target gain matrices without any system dynamics information. In addition, the obtained gain matrices and Lyapunov function are employed to demonstrate that the MASs can reach security consensus. Moreover, the proof of H∞ consensus under undirected graphs is derived using the designed Q-learning algorithm. In the end, the simulation experiments are given to verify the correctness of the designed control strategy.

DoS attack detection using online learning techniques in wireless sensor networks

Wireless sensor network (WSN) models pose substantial security vulnerabilities since most WSNs are deployed in unattended hostile environments. This research focuses on denial-of-service (DoS) attack detection, a crucial problem in WSN security. Previous research has mostly focused on offline machine learning algorithms, which require long-term data collection and cannot continually adapt to new data. Online learning is thus more suitable for detecting DoS attacks in WSN due to the benefit of continuous improvement with fresh data. Nevertheless, existing online DoS attack detection algorithms do not take internal and external data interference into consideration. Thus, noisy data might have a negative effect on the performance of the model. Moreover, the data includes features that are redundant or unnecessary for the classification. Hence, the selection of proper features not only decreases computational time but also improves the performance of the model. This study proposes an online-learning-based approach for detecting DoS attacks in WSN. Specifically, a feature selection method is proposed to identify the most appropriate attributes for the training process. Furthermore, a noise-tolerant online passive-aggressive multi-class classifier is also developed. The performance of our proposed method is investigated in terms of accuracy, precision, recall, and F1-score, and it proves to be competitive.

Cluster synchronization control for coupled genetic oscillator networks under denial-of-service attacks: Pinning partial impulsive strategy

This paper focuses on the security cluster synchronization (CS) of coupled genetic oscillator networks (GONs) with parameters mismatch under denial-of-service (DoS) attacks. Firstly, a model of DoS attacks on the control channel of GONs is established. Then, in order to achieve secure CS of GONs under DoS attacks, a pinning partial impulsive control (PPIC) strategy is proposed for the first time. Compared with the existing pinning impulsive control and partial impulsive control, the PPIC control method only controls part of the molecules of part of the genetic oscillators, so it has better performance in saving control cost. Moreover, we design an algorithm to obtain sufficient conditions for secure CS and the DoS attacks duration ratio that the system can tolerate. In addition, when the system is immune to DoS attacks, it can still achieve CS under PPIC. Finally, two numerical examples based on MATLAB software are presented to demonstrate the effectiveness of the results. The secure synchronization region is obtained, and the convergence efficiency of error systems under PPIC and pinning impulsive control is compared in the simulation.

FVF-AKA: A Formal Verification Framework of AKA Protocols for Multi-server IoT

As IoT in a multi-server environment increases resources’ utilization, more and more problems of IoT authentication and key agreement are being revealed. The Authentication and Key Agreement (AKA) protocol plays an important role in solving these problems. Many AKA protocols have been proposed, and some of them support their own verifications. However, a unifying verification framework for multi-server IoT is lacking. In this article, we propose a formal verification framework of AKA protocols for multi-server IoT (FVF-AKA). It supports the construction of CSP models for the AKA protocol, the implementation of the CSP models in PAT with C#, and the verification of formal models. With the help of C#, many complex functions in the AKA protocol can be implemented. We also design an algorithm to support automatic conversion from the CSP model to the PAT model. FVF-AKA can verify four fundamental properties (deadlock freedom, entity legitimacy, timeout delay, and session key consistency). It also supports the verification of security properties for the AKA protocol suffering from four different attacks (relay attacks, denial of service attacks, server spoofing attacks, and session key attacks). Our approach can be applied to most AKA protocols for multi-server IoT generally. By applying FVF-AKA to two AKA protocols, we can verify whether they satisfy the fundamental properties and analyze their security properties in vulnerable environments. Our work would help to analyze the AKA protocol for multi-server IoT and provide the foundation for the analysis of enhancing its security and robustness.

Event-triggered control design with varying gains for polynomial fuzzy systems against DoS attacks

This paper presents an innovative event-triggered control scheme for addressing the stabilization problem of polynomial fuzzy systems under the influence of Denial-of-Service (DoS) attacks. The proposed controller utilizes a sampling-based event-triggered mechanism to reduce communication resources and avoid Zeno behavior. Furthermore, a novel polynomial fuzzy model-based control system is developed to investigate the impact of periodic DoS attacks and the addressed event-triggered mechanism on system stability. To improve system performance, control gains are updated at each triggering instant. The exponential stability criteria are formulated in the form of sum-of-square constraints, supported by a triggering instant dependent piecewise Lyapunov-Krasovskii functional and an online asynchronous premise reconstruction approach. Finally, the efficiency and usefulness of the theoretical findings are validated through simulation examples.