DoS Attacks Research Articles

Event-Based Distributed Secure Control of Unmanned Surface Vehicles With DoS Attacks

Event-Based Distributed Secure Control of Unmanned Surface Vehicles With DoS Attacks

Nash Equilibrium Seeking for Multi-Agent Systems Under DoS Attacks and Disturbances

Nash Equilibrium Seeking for Multi-Agent Systems Under DoS Attacks and Disturbances

Secure Predictive Coordinated Control of High-Order Fully Actuated Networked Multiagent Systems Under Random DoS Attacks.

This research addresses a coordinated control problem for high-order fully actuated networked multiagent systems (HOFANMASs) under random denial-of-service (DoS) attacks. A type of Bernoulli processes is exploited to denote the successful rate of launching random DoS attacks happened to the forward and feedback channels. When acting these attacks successfully, random data losses and disorders are caused in the forward and feedback channels. A high-order fully actuated (HOFA) secure predictive coordinated control scheme is provided to achieve the security coordination. In this scheme, a dynamic model of networked multiagent system is established with the help of a HOFA system model, which is called the HOFANMAS. Then, a prediction model in an incremental HOFA (IHOFA) form is developed by means of a Diophantine equation, which aims at constructing the multistep ahead output predictions for the optimization of coordinated control performance and the compensation of random data losses and disorders. Furthermore, a necessary and sufficient condition is proposed to analyze the consensus and stability of closed-loop HOFANMASs. The effectiveness and superiority of HOFA secure predictive control scheme can be demonstrated via simulated and experimental results of formation control for three air-bearing spacecraft (ABS) simulators.

Resilient Control for Networked Unmanned Surface Vehicles With Dynamic Event-Triggered Mechanism Under Aperiodic DoS Attacks

Resilient Control for Networked Unmanned Surface Vehicles With Dynamic Event-Triggered Mechanism Under Aperiodic DoS Attacks

Secure Predictive Control for Networked High-Order Fully Actuated Systems Under Random DoS Attacks

Secure Predictive Control for Networked High-Order Fully Actuated Systems Under Random DoS Attacks

Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems

Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important. This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.

Study the Level of Network Security and Penetration Tests on Power Electronic Device

This work demonstrates the feasibility of using Kali Linux in the process of power electronic device research. The novelty in this work is the use of Kali Linux in the process of power electronic device research. This operating system is mainly used for the penetration testing of various communication devices but not for power electronic device research. The aim of this work is to study the level of network security (the type of security vulnerabilities that a power electronic device has) and whether the data exchange between the power electronic device and the monitoring and control center is secure. Additionally, penetration testing has been carried out. Kali Linux was used to implement these tasks. Penetration testing was performed to verify how the studied power electronic device reacted to various TCP DoS attacks—could it be accessed, was it blocked, etc. Kali Linux and some of the tools built into the operating system—Nmap, hping3, Wireshark, Burp Suite Community Edition—were used for this study. During the penetration tests, a characterization of the traffic being processed/generated by the studied power electronic device was carried out to evaluate and analyze what impact each TCP DoS attack had on the device’s performance. In order to conduct the study, an experimental setup was designed. This experimental network was not connected to other networks, so the cyber attacks were controlled and confined within the experimental network. The research carried out validated the use of Kali Linux for the study of power electronic devices. From the obtained results, it is found that the studied power electronic device provides a certain level of network security, but the data exchange is insecure.

Event-triggered-based encoding–decoding consensus control of continuous-time multi-agent systems under DoS attacks

The security of multi-agent systems (MASs) is important particularly when considering the potential threat posed by malicious attackers. Specifically, network attacks and information theft caused by attackers have garnered significant attention due to their potential to destroy the consensus of the system. Therefore, this paper presents a novel approach to achieve leader-following consensus control of continuous-time MASs under DoS attacks, leveraging event-triggered encoding and decoding techniques. When the DoS attacks occur, the transmitted information between agents becomes unavailable. To address this challenge, a buffer is introduced to store the latest transmission data and then the stored data can be used when the DoS attacks exist. We also propose an event-triggered encoding–decoding strategy that can be employed for privacy protection in linear continuous-time MASs. The transmitted data is encoded as a codeword before transmitting, and the encoder updates it when event-triggered conditions are met. The receiving end decodes the data upon receipt. Furthermore, a distributed switching controller is designed using the encoded and stored state values to achieve leader-following consensus. The control gain is calculated to obtain sufficient conditions for DoS attacks through the linear matrix inequalities (LMI) and Lyapunov functions. The boundedness of transmitted data and the Zeno phenomenon are also analyzed. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed control algorithm.

Ensemble classification to predict botnet and its impact on IoT networks

The IoT (Internet of Things) has been rapidly growing to make a stronger influence on huge industrial systems. Since cybercriminals have made IoT a target for harmful operations such as botnets, an attack on the end nodes is now a possibility. Protecting IoT infrastructure with a typical intrusion detection system is difficult because of its vastness, variety, and minimal resource availability. Harmful "bot sources” (servers) control a botnet, which is made up of hacked devices that are employed for several illicit purposes like sending spam, initiating DoS attacks, and stealing personal data. Because bot sources generate network traffic while conversing with their bots, Intrusion Detection Systems could benefit from analyzing traffic on the network to detect Botnet traffic. DDoS attacks and spam distribution are common uses for botnets.As machine learning (ML) approaches are employed in various niches of security, it appears practical and workable to use ML to detect botnets. ML has been used in various researches to detect botnets. However, the results are either unreliable or limited to certain types of botnets or devices. Current approaches attempt to solve these issues by presenting models that are trained on botnet features, but the large dimensionality of feature values and the reliance on botnet features alone are serious drawbacks. In order to address these constraints, a novel botnet and its impact detection by a typical ensemble classification approach has been proposed by the current work. The framework uses the correlated traffic-flow and botnet features to train the classifier, which is titled as "Ensemble Classification to Predict Botnet and its Impact (EC-PBI) on IoT Networks". The experimental study is a cross validation approach that is performed using a multi-label, fourfold strategy. Performance analysis of the proposed approach was done by comparing it with contemporary models. Results prove its efficiency in detecting botnet and its impact.

Robust [formula omitted] control of singular networked cascade control systems with norm-bounded uncertainty and periodic DoS attack

In this article, the topic of event-triggered H∞ control is investigated for a singular networked cascade control system (SNCCS) under periodic DoS attacks, norm-bounded uncertainty, and external disturbances. Unlike existing articles, the SNCCS is assumed to be singular not only in the inner loop but also in the outer loop. Then, using Multiple Lyapunov Functionals (MLF), new sufficient conditions are derived for the admissibility of the SNCCS through linear matrix inequalities, guaranteeing the regularity and impulse-freeness of the system. Afterward, state-feedback controllers are designed for both the inner and the outer loops of the system. All the derived theories in this article can be utilized for stabilizing both singular and non-singular NCCSs. Finally, simulation results are presented on a steam generation process in a power plant to validate the proposed method.

Optimal synchronization for multi-agent systems: A performance-dependent switching topology

This paper proposes an optimal output synchronization control method for heterogeneous multi-agent systems (HMASs) under a performance-dependent switching topology and DoS attacks. First, local and global switched performance index functions (SPIF) and SPIF-dependent topology switching law are proposed, respectively, thus, the control performance and topology quality can be quantitatively expressed. Second, an adaptive dynamic programming (ADP) algorithm with mode switching is proposed, aimed at dealing with the difficult Hamilton–Jacobi–Bellman equation, as well as the analytical complexity caused by the switching dynamics. The convergence of the switched ADP algorithm is proven to ensure its correct implementation. Then, for different topologies, multi-mode Actor–Critic neural networks (NNs) are built for each agent to calculate optimized control policies and SPIF, respectively. Furthermore, an NN-based state compensation mechanism is designed to expand the applicability of the designed switched ADP algorithm when the leader’s output transmission is unreliable. Finally, the results of numerical examples confirm that the proposed method is feasible.

Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks

Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks

Adaptive Decentralized Fuzzy Resilient Control for Large-Scale Nonlinear Systems Against Stochastic Disturbances and DoS Attacks

Adaptive Decentralized Fuzzy Resilient Control for Large-Scale Nonlinear Systems Against Stochastic Disturbances and DoS Attacks

Event-Triggered Guaranteed Cost Secure Consensus Control of Multiagent Systems Under DoS Attacks Over Multiple Communication Channels

Event-Triggered Guaranteed Cost Secure Consensus Control of Multiagent Systems Under DoS Attacks Over Multiple Communication Channels

Observed-Mode-Dependent Nonfragile Control of Networked Control Systems Under Hidden DoS Attacks

This paper develops a methodology on observed-mode-dependent non-fragile control for networked control systems subject to denial-of-service attacks, which takes the strong concealment of attack behavior into account. To describe complex switching phenomenon between different forms of attacks that the adversary may launch on communication channels, the semi-Markov chain is introduced. DoS attacks with stealth characteristic is analyzed from hidden semi-Markov jump systems perspective by constructing a double-layer stochastic process consisting of semi-Markov chain and observed mode sequence. With the aid of emission probability, an observed-mode-dependent non-fragile controller is designed, which can still stabilize the system even if actual attack modes are unavailable. By utilizing the Lyapunov function that depends on actual attack modes and observed ones, sufficient conditions to ensure the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -error mean square stability of the closed-loop system is deduced. Eventually, a simulation related to the mass-spring-damper system with single channel and an example of the unmanned ground vehicle with two channels are studied, both of which demonstrate the feasibility and validity.

Fuzzy Adaptive Resilient Formation Control for Nonlinear Multiagent Systems Subject to DoS Attacks

Fuzzy Adaptive Resilient Formation Control for Nonlinear Multiagent Systems Subject to DoS Attacks

$H_\infty$ Control for Fuzzy Switched Cyber-Physical Systems Subject to DoS Attacks

$H_\infty$ Control for Fuzzy Switched Cyber-Physical Systems Subject to DoS Attacks

Switching-Like Event-Triggered Sliding Mode Load Frequency Control for Networked Power Systems Under Energy-Limited DoS Attacks

Switching-Like Event-Triggered Sliding Mode Load Frequency Control for Networked Power Systems Under Energy-Limited DoS Attacks

Finite-Time Distributed Filtering of Time-Varying Switched Stochastic Systems With DoS Attacks Based on Encoding–Decoding Scheme

Finite-Time Distributed Filtering of Time-Varying Switched Stochastic Systems With DoS Attacks Based on Encoding–Decoding Scheme