Denial Of Service Attacks Research Articles

Lightweight Machine Learning-Based DOS Attack Detection in Wireless Sensor Networks Using Decision Tree and Information Gain

Abstract—Wireless Sensor Networks (WSNs) are rapidly expanding across various domains due to their unique characteristics and performance capabilities. However, these networks are highly vulnerable to a range of security threats, particularly Denial-of-Service (DoS) attacks, which are among the most common in WSNs. This paper explores the vulnerabilities of WSNs, focusing on DoS threats, and reviews current techniques for their detection. It introduces a lightweight machine learning-based approach using a decision tree (DT) algorithm with the Information Gain (IG) feature selection method for efficient DoS detection. Tested on an enhanced WSN-DS dataset, the proposed method demonstrated high accuracy and minimal processing time compared to other classifiers, such as XGBoost, and RF. This efficiency makes the proposed method well-suited for real- time DoS attack detection in resource-constrained WSNs. Keywords—Machine Learning,Decision Tree (DT),Information Gain (IG),DoS attack detection

Privacy protection-based resilient secondary control for DC microgrids under DoS attacks

In this paper, we investigate the privacy protection-based resilient secondary control problem for an isolated DC microgrid (MG) under denial-of-service (DoS) attacks. To solve the problem, a resilient privacy protection control method is proposed by introducing random noises and the state decomposition strategy. Specifically, the initial secondary control signal of each distributed generator (DG) includes two parts: one is utilized for neighbor communication and compensating for the voltage deviation caused by the droop control, while the other influences the evolution of the first part and remains undetected to the neighbors. Besides, to ensure that the true value of the transmitted current remains obscured, random noises are injected in the first part. Based on the developed privacy protection mechanism, a distributed resilient controller is designed to guarantee the deviations in current and voltage are effectively regulated within the permissible range under DoS attacks, while simultaneously safeguarding the privacy datas. Ultimately, the efficiency of our proposed method is proved through real-time experiments conducted in OPAL-RT 4510.

Advanced security framework for low Earth orbit satellites in space information network

Recently, low Earth orbit (LEO) satellites have emerged as key players in space information network (SIN) due to their ability to provide global coverage. However, they remain susceptible to threats such as denial of service (DoS), man-in-the-middle (MITM), and spoofing attacks. In this paper, we propose a cross-layer security framework (CLSF) to address these vulnerabilities. Our approach begins by employing a physically unclonable function (PUF) at the upper layer to establish mutual authentication between legitimate satellites and ground stations, while also securely exchanging frequency seeds for the next phase. Following this, dynamic seed frequency hopping (DSFH) is applied at the physical layer to counter DoS, MITM, and spoofing attacks. Additionally, the frequency transitions of malicious satellites are modeled using a Markov chain. Our results demonstrate that the proposed CLSF, which integrates PUF and DSFH, delivers strong security performance.

Ultimately bounded control for nonlinear systems under denial-of-service attacks: an accumulation-based event-triggered mechanism

This paper investigates the control problem for nonlinear systems under denial-of-service (DoS) attacks. In contrast to traditional event-triggered mechanisms (ETMs), an accumulation-based ETM, which is robust to abrupt signals, is employed to schedule the signal transmission between sensors and controllers, thereby saving more communication resources. Due to the openness of network-based transmission, DoS attacks are encountered during data transmissions, where the DoS attacks considered in this paper follow the Bernoulli distribution. This paper aims to design a controller for nonlinear systems considering the impact of DoS attacks and accumulation-based ETMs, ensuring that the controlled system state is ultimately bounded in the mean square. Firstly, sufficient conditions are provided to guarantee that the system state can achieve the given control performance, along with an explicit expression for the ultimate bound. Then, the controller gains are obtained by solving certain linear matrix inequalities. Finally, two simulation examples are conducted to validate the effectiveness of the designed controller.

Fully distributed event-triggered secondary voltage resilient tracking control for microgrids

This paper aims to design the fully distributed event-triggered secondary voltage resilient tracking control strategy for microgrids subject to denial-of-service (DoS) attacks. The fully distributed method is employed to remove the requirement of the smallest nonzero eigenvalue of the Laplacian matrix, which is the global information. In addition, an attack compensation with the latest successful transmission state information from neighbor's as the attack compensation term is designed for both the event-triggered scheme and the controller to save communication resources and mitigate the negative effects caused by DoS attacks. Finally, the effectiveness of the designed fully distributed event-triggered resilient control method subject to DoS attacks is demonstrated via a simulation example with comparisons.

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber deception using honey drones (HDs) to protect the mission system from DoS attacks. HDs exhibit fake vulnerabilities and employ stronger signal strengths to lure DoS attacks, unlike the legitimate drones called mission drones (MDs) deployed for mission execution. This research formulates an optimization problem to identify an optimal set of signal strengths of HDs and MDs to best prevent the system from DoS attacks while maximizing mission performance under the resource constraints of UAVs. To solve this optimization problem, we leverage deep reinforcement learning (DRL) to achieve these multiple objectives of the mission system concerning system security and performance. Particularly, for efficient and effective parallel processing in DRL, we utilize a DRL algorithm called the Asynchronous Advantage Actor-Critic (A3C) algorithm to model attack-defense interactions. We employ a physical engine-based simulation testbed to consider realistic scenarios and demonstrate valid findings from the realistic testbed. The extensive experiments proved that our HD-based approach could achieve up to a 32% increase in mission completion, a 20% reduction in energy consumption, and a 62% decrease in attack success rates compared to existing defense strategies.

Embedding Tree-Based Intrusion Detection System in Smart Thermostats for Enhanced IoT Security.

IoT devices with limited resources, and in the absence of gateways, become vulnerable to various attacks, such as denial of service (DoS) and man-in-the-middle (MITM) attacks. Intrusion detection systems (IDS) are designed to detect and respond to these threats in IoT environments. While machine learning-based IDS have typically been deployed at the edge (gateways) or in the cloud, in the absence of gateways, the IDS must be embedded within the sensor nodes themselves. Available datasets mainly contain features extracted from network traffic at the edge (e.g., Raspberry Pi/computer) or cloud servers. We developed a unique dataset, named as Intrusion Detection in the Smart Homes (IDSH) dataset, which is based on features retrievable from microcontroller-based IoT devices. In this work, a Tree-based IDS is embedded into a smart thermostat for real-time intrusion detection. The results demonstrated that the IDS achieved an accuracy of 98.71% for binary classification with an inference time of 276 microseconds, and an accuracy of 97.51% for multi-classification with an inference time of 273 microseconds. Real-time testing showed that the smart thermostat is capable of detecting DoS and MITM attacks without relying on a gateway or cloud.

Observer-based resilient cooperative output regulation for nonlinear multiagent systems with time-varying parameters and DoS attacks

This paper aims to address the cooperative output regulation (COR) problem of nonlinear multiagent systems (MASs) with time-varying parameters under denial-of-service (DoS) attacks. First, to address the challenges of unavailable leader signals and non-differentiability issue caused by the DoS attacks, a class of first-order low-pass filters is employed to develop a novel distributed resilient observer, which accurately estimate the exosystem state with the existence of high-order derivatives. Then, by viewing the output of observer as the reference signal, a distributed resilient controller is designed for each follower to address the reference-tracking problem. By introducing a smooth function with positive integrable time-varying function, novel adaptive laws are developed and incorporated with the controller design to effectively compensate for the unknown time-varying parameters. By utilising Lyapunov stability theory, the asymptotic convergence of output regulation error can be ensured. Finally, simulation results are conducted to verify the validity of the designed method.

Resilient Event‐Triggered Control for Switched Systems With Reconstructed Switching Signals Under DoS Attacks

ABSTRACTIn this article, the resilient event‐triggered control issue for switched linear systems with the bounded disturbance is explored under the aperiodic denial‐of‐service (DoS) attacks. Unlike the discussions on asynchronous behavior caused by network transmission/DoS attacks, configure a copy of predictor at controller module to avert the negative impacts of asynchronous switching. Subsequently, by establishing a resilient event‐triggered strategy (RETS) to eliminate invalid sampling during DoS attacks intervals, the waste of network resources is reduced. Besides, a sufficient condition is derived to ensure the input‐to‐state stability (ISS) for the close‐loop switched systems under DoS attacks. The results indicate that the switched systems can resist a certain degree of DoS attacks under the average dwell time (ADT) constrained by DoS attacks frequency and duration. In addition, Zeno behavior is eliminated by calculating the lower bound of the triggering intervals. Finally, the availability of theoretical approach is verified through a numerical simulation.

Robust model predictive bipartite consensus control for cooperation-competition MASs under DoS attacks

Abstract This paper addresses the bipartite consensus control problem for a class of discrete-time cooperation-competition multi-agent systems (MASs) subject to denial-of-service (DoS) attacks under the robust model predictive control (RMPC) framework. A signed graph is characterized to describe the cooperation-competition relationship, and the edges of the graph have either positive or negative connection weights. A DoS attacker accomplishes their objectives by obstructing the communication channel between the controller and the actuator. Meanwhile, with the goal of achieving accurate bipartite consensus, an observer-based RMPC algorithm is aimed to be designed in the context of the DoS attacks, which ensures the recursive feasibility and input-to-state stability (ISS) in the mean square sense. The effectiveness of the algorithm is verified by an example in the end.

Novel Dynamic Event-Triggered Consensus Control of Multiagent Systems With Markovian Switching Topologies Under DoS Attacks.

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

IPv6 addressing strategy with improved secure duplicate address detection to overcome denial of service and reconnaissance attacks

With technology development, the growing self-communicating devices in IoT networks require specific naming and identification, mainly provided by IPv6 addresses. The IPv6 address in the IoT network is generated by using the stateless auto address configuration (SLAAC) mechanism, and its uniqueness is ensured by the DAD protocol. Recent research suggests that IPv6 deployment can be a risky decision due to the existing SLAAC-based addressing scheme and the DAD protocol being prone to reconnaissance and denial of service (DoS) attacks. This research paper proposes a new IPv6 generation scheme with an improved secure DAD mechanism to address these problems. The proposed addressing scheme generates IPv6 addresses by taking a hybrid approach based on vendor id of medium access control (MAC) address, physical location, and arbitrary random numbers, which mitigates reconnaissance attacks by malicious nodes. To prevent the DAD process from DoS attacks, hybrid values of interface identifier (IID) are multicast instead of actual values. The proposed scheme is evaluated under reconnaissance and DoS attacks in the presence of malicious nodes. The evaluation results demonstrate that the proposed method effectively mitigates reconnaissance and DoS attacks, outperforming the EUI-64 and SEUI-64 schemes in terms of address success rate (ASR), energy consumption, and communication overhead. Specifically, the proposed method significantly reduces the average probing rate for scanning the existence of an IPv6 address, with only a 1% probing rate compared to SEUI-64’s 5% and EUI-64’s 100%. Furthermore, the additional communication overhead introduced by the proposed method is less than 13% and 11% compared to EUI-64 and SEUI-64, respectively. Additionally, the energy consumption required to assign an IPv6 address using the proposed method is lower by 12% and 5% when compared to EUI-64 and SEUI-64, respectively. These findings highlight the effectiveness of the proposed method in enhancing security and optimizing resource utilization in IPv6 addressing.

Fuzzy adaptive asynchronous sampled-data consensus control for nonlinear multi-agent systems under DoS attacks

This article investigates the problem of fuzzy adaptive resilient asynchronous sampled-data consensus control (SDCC) for a class of uncertain nonlinear strict-feedback multi-agent systems (MASs) under denial-of-service (DoS) attacks and with a directed graph. Firstly, the upper bounds on the sampling intervals and a distributed sampled-data observer are proposed to estimate the leader's output and its high-order derivatives under DoS attacks. Then, the sufficient conditions to ensure the asymptotic convergence of the designed distributed sampled-data observer are established. Based on the distributed sampled-data observer and backstepping control design technique, a distributed fuzzy adaptive resilient control algorithm is developed. It is proved that the proposed control scheme can guarantee that the controlled MASs are stable, and the consensus errors converge to a small neighbourhood of zero. Finally, a simulation example on heterogeneous vehicular platoons verifies the feasibility of the proposed control scheme.

Event-triggered bipartite consensus to heterogeneous multiagent systems under DoS attacks: A fully distributed method

This paper studies event-triggered bipartite output consensus problem of heterogeneous multiagent systems under denial-of-service (DoS) attacks. A novel dynamic event-triggered scheme (DETS) is proposed, which, by introducing an extra dynamic function with time-varying coefficients into triggering conditions, can guarantee strictly positive minimum inter-event intervals no matter DoS attacks occur or not. An event-based resilient compensator with adaptive coupling coefficients is then designed to estimate leader's state, and a hybrid model with jump dynamics is constructed that can incorporate the estimation error, DETS, and DoS attacks, and is useful for convergence analysis. Then, a fully distributed observer-based control protocol is designed to regulate the bipartite output consensus. The main advantages of the proposed method include: 1) global information is not needed to implement the event-based control protocol; 2) strictly positive inter-event intervals are guaranteed even under DoS attacks. Finally, a numerical example is presented to testify the main results.

Resilient consensus of heterogeneous multi-agent systems under asynchronous optimal DoS attack schedules

This study aims to address the challenge of achieving resilient consensus in heterogeneous multi-agent systems (MASs) under asynchronous optimal Denial of Service (DoS) attack schedules. Considering the start and duration of adversaries vary from different communication channels of MASs, we mainly focus on how adversaries with limited energy choose channels to launch attacks to cause the greatest damage to system performance. Based on dynamic programming, an asynchronous optimal DoS attack schedules generation algorithm is obtained. Optimal DoS attack schedules provides a basis for designing more effective secure control methods. Then, a distributed resilient control strategy that utilizes an observer-based approach to achieve output consensus in heterogeneous MASs under asynchronous optimal DoS attack schedules is proposed. Additionally, we establish conditions regarding the durations and frequencies of DoS attacks as well. Finally, two simulations are performed to showcase the efficacy of our proposed method.

Resilient Discrete‐Time Quantization Communication for Distributed Nash Equilibrium Seeking Under DoS Attacks

ABSTRACTThis paper studies the design of resilient distributed Nash equilibrium (NE) algorithm for second‐order multi‐agent systems in the presence of Denial‐of‐Service (DoS) attacks, which prevent information transmission in the network with limited bandwidth and transmission rate. Resilient communication schemes, integrating periodic/event‐triggered schemes and a dynamic quantized transmission method, are designed in a distributed NE seeking algorithm to ensure the convergence of agents' strategies to the NE of the game. For the designed two resilient communication schemes, the sufficient conditions for the convergence of the algorithm are established, and the tradeoff between the sampling period and the frequency and duration of DoS attacks is analyzed. Cournot competition with distributed energy resources is used as an example to verify the proposed methods.

Event‐Triggered Secure Control of Positive Networked Control Systems Under Multi‐Channels Attacks

ABSTRACTThis paper focuses on the secure control of positive networked control systems under multi‐channel attacks characterized by a Bernoulli distribution. Specifically, the sensor‐to‐controller channel suffers from false data injection (FDI) attacks, whereas the controller‐to‐actuator channel suffers from denial of service (DoS) attacks. The objective is to design a static output feedback controller that guarantees the positivity and stochastic stability of the closed‐loop system in the presence of DoS and FDI attacks. To conserve communication resources, we employ an event‐triggered scheme to reduce the frequency of information transmission. Due to the positivity of the system, the proposed event‐triggering mechanism employs the 1‐norm form instead of the 2‐norm form, which allows that the controller parameter is determined via linear programming rather than LMI technique. Finally, two simulation examples are provided to verify the effectiveness of our methods.

Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

CYBERSECURITY AWARENESS

Technology is advancing at a very high speed, becoming more sophisticated and complex. Similarly, due to the vastness of the digital world, cyber threats have also increased in complexity and frequency. The rapid advancement of technology has brought unprecedented conveniences but also significant risks. Therefore, security awareness is essential for every individual and organization. Cyber threats such as phishing attacks, malware attacks, social engineering, Denial of service attacks, and insider threats have become sophisticated, proving to be a challenge to individuals and organizations. This paper evaluates security awareness in the organization with a keen interest in why the program is essential, the components of the program, challenges that might be faced while implementing the program, and what role the organization and employees have to play to ensure the environment is secure. The paper provides a comprehensive analysis highlighting the importance of security awareness in an organization's current sophisticated digital world. Keywords - Security Awareness, Cyber Threats, Phishing, Malware, Social Engineering, Information Security, Cybersecurity Training, Risk Mitigation, and Continuous Education.