Denial Of Service Attacks Research Articles

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.

Networked Control of a Small Drone Resilient to Cyber Attacks

With increasing advances in networked systems and networked control systems in everyday life, the problem of cybersecurity becomes crucial. Moreover, in some applications like small UAVs, the safety and integrity of the system and its surroundings are highly susceptible to cyberattacks. In this context, the current paper proposes a resilient networked control approach. The control structure is split into an inner and an outer loop. The outer position control loop uses measurements from motion cameras connected to a remote computer, while the commands are sent through the network. We consider the resilience problem for two types of cyberattacks: denial of service (DoS), emulated as an increase in the network transmission delay, and man in the middle (MitM), emulated as additive input disturbances. The mitigation for the DoS attack is performed through the help of a reference governor (RG), which uses the delay estimates and the system’s model to predict future safety violations and adapts the reference accordingly. The MitM attack is mitigated by an unknown input disturbance observer (UIDO) together with a RG. Experimental results on a Parrot Mambo drone show that both types of attacks are rejected successfully, ensuring a safe and stable flight.

Distributed adaptive secure consensus control for multiagent systems under denial-of-service attacks

This paper investigates the security consensus of multiagent systems (MASs) under denial-of-service (DoS) attacks. First, we introduce a novel dynamic consensus protocol against DoS attacks by utilising relative measurement outputs. Then, based on the edge pinning technique, the novel distributed synchronous adaptive laws are designed to tune a small fraction of coupling strengths between neighbouring agents, under which all updating of coupling strengths will stop when DoS attacks are activated. To extend the application of secure dynamic edge pinning consensus scheme, we further develop the distributed asynchronous adaptive laws, under which only the coupling strengths of the attacked edges stop updating rather than all coupling strengths when DoS attacks are activated. Note that the proposed secure dynamic edge pinning consensus protocols with distributed synchronous and asynchronous adaptive laws are fully distributed and operate independently of any global information. At last, we validate the validity of the proposed distributed adaptive secure dynamic consensus schemes via an example.

Symbolic testing of floating-point bugs and exceptions

Numerical software are susceptible to floating-point bugs and exceptions, which may lead to severe threats like denial of service attacks. Static analysis techniques such as symbolic execution are effective in detecting general bugs which often cause memory error or program crash. Unfortunately, these methods do not deal well with numerical code as they do not support floating-point constraints and math functions symbolically. In this paper, we propose a new analysis framework YUSE, which can detect floating-point bugs by constructing constraints and exploring paths which contain floating-point expressions. Specifically, we introduce interval computation and interval constraint propagation in non-relational numerical abstract domains, and symbolically model math functions, to accurately detect floating-point bugs and exceptions. Moreover, we leverage two-phase constraint solving to enhance YUSE’s performance. Experimental results show that YUSE outperforms two state-of-the-art tools, Frama-c and Fpse-study, in terms of effectiveness and efficiency, with 1.4× and 7.1× faster than Frama-c and Fpse-study, respectively. Moreover, YUSE found 20 new bugs in real-world software, 12 of which were assigned CVE IDs and 8 of which were confirmed by developers.

Collision-Free Formation Control for Heterogeneous Multiagent Systems Under DoS Attacks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents' operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.

Secure impulsive synchronization for derivatively coupled networks against DoS attacks: A memory-compensated strategy

This work is concerned with the synchronization issue of complex networks suffering from Denial-of-Service (DoS) attacks. The derivative coupling has been proposed to model the nodes which are capable of perceiving state changing. An overarching handling procedure is initially introduced to convert derivatively coupled dynamic networks into a general form. To offset the information missing resulting from DoS attacks, an intricately crafted memory-based self-triggered impulsive controller (MBSTIC) is devised. The introduced scheme involves the meticulous collection of nodes’ historical state information. By jointly considering Lyapunov stability theorem and mathematical deduction approach, sufficient conditions for ensuring synchronous behavior for controlled networks are derived. A numerical example is given to demonstrate that the proposed method exhibits considerable performance when facing DoS attacks.

Mixed-Zero-Sum-Game-Based Memory Event-Triggered Cooperative Control of Heterogeneous MASs Against DoS Attacks.

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

A variable threshold weight approach to dynamic event-triggered consensus control for leader-following multi-agent systems under asynchronous DoS attacks

This paper is concerned with the event-triggered consensus problem for leader-following multi-agent systems under asynchronous denial-of-service (DoS) attacks. Since the asynchronous attacks on different edges may enlarge the defense burden, the topology connectivity is employed to reveal the effectiveness of attacks and determine the unhealthy time periods. Relying on the internal system state, a dynamic event-triggered mechanism is built to regulate the information transmission between agents. In this mechanism, a variable threshold weight composed of the deviations of the relative neighboring errors and historical state errors is proposed to adaptively schedule the triggering thresholds with systems running stages. To guarantee secure consensus performance even during the triggering intervals, the local estimation of the neighboring information is integrated into the controller such that sufficient conditions to obtain the control parameters and the tolerable attack parameters are established without any Zeno behavior. The proposed method is validated by a simulation study.

Collaborative Intrusion Detection System with Snort Machine Learning Plugin

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.

Adaptive control of networked control systems under DoS attacks

This research introduces a new adaptive control approach tailored for networked control systems, aimed at addressing the challenges of Denial of Service (DoS) attacks and round-trip time delays. Initially, a compensation mechanism based on estimated values is introduced to counter DoS attacks; subsequently, for round-trip time delay issues, a predictive delay compensation control strategy is designed. The proposed control strategy does not rely on the specific structure or a particular model of the system, but instead solely updates the controller using the system's input and output values. Moreover, we successfully demonstrate the bounded stability of the system using the direct Lyapunov method. Ultimately, the effectiveness and practicality of this method are confirmed through simulation results.

Recursive state estimation for delayed complex networks with random link failures and stochastic inner coupling under cyber attacks

This paper focuses on the filtering issue regarding a class of uncertain time-delayed complex networks (CNs) subject to random link failures (RLF) and stochastic inner coupling (SIC) under cyber attacks, where the attacks occur in a random way with uncertain occurrence probabilities. The phenomena of RLF and SIC are described via a Bernoulli distributed random variable and a Gaussian noise, respectively. The former reflects the presence or absence of connections between different nodes, while the latter presents the uncertainty of the inner coupling strength. In addition, denial of service attacks (DoSAs) and false data injection attacks (FDIAs) are both taken into account in the discussed cyber space, where the switching behavior of attack modes is described by two independent Bernoulli distributed random variables. A novel recursive estimator is constructed with consideration of RLF, uncertain time-delay, SIC and cyber attacks, wherein a state estimation error covariance upper bound (SEECUB) is obtained. And such upper bound is minimized via reasonable design of the estimator gain. Through theoretical deduction and analysis, it is proved that the presented SEECUB is uniformly bounded under certain conditions. Finally, the availability of the proposed filtering strategy and the gained results are verified through simulation examples.

Improved event-based fault detection filter for networked fuzzy systems under DoS attacks

This paper investigates an improved event-based fault detection method for networked fuzzy systems under denial-of-service (DoS) attacks. In order to solve the bandwidth occupation problem of communication network, a resilient event-triggered transmission strategy is developed. Additionally, a fault detection filter is designed to estimate the time of fault occurrence by using the residual signal. Under this framework, a novel Lyapunov functional related to attack parameters is established to analyze the exponential convergence of the error signals, and the filter gains and event-triggered parameters are obtained by solving linear matrix inequalities. The designed functional reduces the conservatism of the stability criteria significantly in contrast with the previous discontinuous Lyapunov functionals. Finally, a simulation example is provided to verify the effectiveness of the proposed method.