外部干扰和随机DoS攻击下的网联车安全H∞队列控制

针对网联车队列系统易受到干扰和拒绝服务(Denial of service, DoS)攻击问题,提出一种外部干扰和随机DoS攻击作用下的网联车安全H∞队列控制方法.首先,采用马尔科夫随机过程,将网联车随机DoS攻击特性建模为一个随机通信拓扑切换模型,据此设计网联车安全队列控制协议.然后,采用线性矩阵不等式(Linear matrix inequality, LMI)技术计算安全队列控制器参数,并应用Lyapunov-Krasovskii稳定性理论,建立在外部扰动和随机DoS攻击下队列系统稳定性充分条件.在此基础上,分析得到该队列闭环系统的弦稳定性充分条件.最后,通过7辆车组成的队列系统对比仿真实验,验证该方法的优越性.

计及DoS攻击和通信时滞的电力系统负荷频率控制

针对电力系统通信网络中存在的时滞和DoS攻击现象,本文提出了基于采样特性的电力系统离散负荷频率控制方案。首先,计及电力系统负荷波动的情况,在多区域电力系统负荷频率控制系统(LFC)模型的基础上,将通信网络中DoS攻击的危害量化为采样信号连续丢失数,并分别考虑通信网络中通信时滞和输出信号采样特性,建立包含输出状态反馈控制器的电力系统LFC模型。其次,基于此模型,利用含有控制命令更新周期、DoS攻击导致的最大采样信号连续丢失数、通信时滞、H_(∞)稳定指标的双边闭环Lyapunov泛函、LMI技术,提出LFC系统满足一定H_(∞)指标的稳定准则,并给出离散状态输出反馈LFC控制器的设计及求解方法。最后,本文以单区域和双区域LFC系统为例进行了仿真验证。结果表明:与已有文献方法相比,本文方法在保持系统H_(∞)渐近稳定前提下,能够容忍更大的通信时滞,且结果的保守性更低;同时可以看出本文所设计的控制器在保证H_(∞)稳定性能的基础上,对一定能量限制的DoS攻击具有弹性的防御性能。因此本文方案的有效性和优越性得到了验证。

DoS攻击下抛物型多智能体系统的输出反馈领导-跟随一致性

为了解决抛物型多智能体系统(MASs)在拒绝服务(DoS)攻击下的一致性问题,提出了一种基于输出反馈方法的MASs领导-跟随一致性控制协议来应对恶意攻击者发起的DoS攻击,从而实现智能体在无向通信拓扑图上的领导-跟随一致性。对DoS攻击的频率和持续时间进行了分析,利用Lyapunov稳定性定理推导了抛物型偏微分系统的稳定条件,证明了触发时间间隔不为零,避免了芝诺行为的发生,并通过仿真算例验证了理论结果的有效性。

基于联邦学习的无线通信网络DoS攻击检测方法

无线通信网络受到DoS攻击,会使得网络的负载增加,导致延迟增加。而在无线通信网络中,数据通常分散在多个节点上,这会造成数据泄露和被攻击。为此,提出一种基于联邦学习的无线通信网络DoS攻击检测方法。对初始无线通信网络数据进行预处理和归一化,并采用随机森林算法进行降维处理,去除冗余特征,获得最佳网络数据特征集。将特征集输入到以深度卷积神经网络为通用模型的联邦学习训练模型中,独立训练本地模型并进行模型修正,传输至中心服务器进行聚合,收敛后完成训练。利用训练得到的联邦学习模型检测无线通信网络DoS攻击速率,再与接收者接收的容量最大值进行比较,判断是否有DoS攻击。实验结果表明,所提方法在处理大量数据时具有较高的稳定性和可靠性,能够在短时间内准确地检测出DoS攻击。

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

带DoS攻击补偿的线性离散系统分布式融合滤波器

多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最小方差矩阵加权融合算法获得分布式融合状态滤波器。所提出的分布式融合滤波器改善了局部估计的精度,且比协方差交叉融合算法具有更高的估计精度。仿真例子验证了算法的有效性。

无界DoS攻击下的IPv6网络多层协同防御模型构建

由于无界DoS攻击具有高度的隐蔽性和复杂性,单层防御无法解决入侵问题,因此构建了无界DoS攻击下的IPv6网络多层协同防御模型。根据协同防御思想,构建IPv6网络多层协同防御模型;收集模块流量,利用DNN模型对IPv6网络流量异常情况进行检测;引入协同式HCF自学习算法,以实现IPv6网络防御信息共享与异常网络流量过滤;利用攻击受损关联度的强化学习算法进行IPv6网络防御。实验结果表明,该模型可在2 ms内恢复CPU;在遭受不同类型的DoS攻击后IPv6网络吞吐量始终高于200 KiB s。这说明该模型可有效抵御不同类型的DoS攻击,反应迅速,防御效果好。

电力系统防御DoS网络攻击的机制研究

传统电力系统容易受到网络干扰和攻击,系统中某一部分受到攻击可能会导致整个电力系统瘫痪。由于现代电力系统的广域性和灵活性会导致出现更多的网络攻击点,因此针对新领域研究更多的防御策略变得至关重要。基于此,利用连续时域模型对各种攻击策略进行建模,并分析电力系统防御拒绝服务(Denial of Service,DoS)攻击的机制。

DoS Attack Detection Based on Deep Factorization Machine in SDN

Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)attacks.While many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks.To solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in SDN.Firstly,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection feature.Then,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks.After training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is implemented.Simulation results show that our method can effectively detect DoS attacks in SDN.Compared with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values.

Honeypot Game Theory against DoS Attack in UAV Cyber

A space called Unmanned Aerial Vehicle(UAV)cyber is a new environment where UAV,Ground Control Station(GCS)and business processes are integrated.Denial of service(DoS)attack is a standard network attack method,especially suitable for attacking the UAV cyber.It is a robust security risk for UAV cyber and has recently become an active research area.Game theory is typically used to simulate the existing offensive and defensive mechanisms for DoS attacks in a traditional network.In addition,the honeypot,an effective security vulnerability defense mechanism,has not been widely adopted or modeled for defense against DoS attack UAV cyber.With this motivation,the current research paper presents a honeypot game theorymodel that considersGCS andDoS attacks,which is used to study the interaction between attack and defense to optimize defense strategies.The GCS and honeypot act as defenses against DoS attacks in this model,and both players select their appropriate methods and build their benefit function models.On this basis,a hierarchical honeypot and G2A network delay reward strategy are introduced so that the defender and the attacker can adjust their respective strategies dynamically.Finally,by adjusting the degree of camouflage of the honeypot for UAV network services,the overall revenue of the defender can be effectively improved.The proposed method proves the existence of a mixed strategy Nash equilibrium and compares it with the existing research on no delay rewards and no honeypot defense scheme.In addition,this method realizes that the UAV cyber still guarantees a network delay of about ten milliseconds in the presence of a DoS attack.The results demonstrate that our methodology is superior to that of previous studies.

DoS攻击下火电-光伏电力系统模型预测负荷频率控制技术

随着新能源及数字化技术在电力系统中的广泛应用,系统的随机性、波动性、分散性明显增强,给电网频率安全稳定带来巨大挑战。针对DoS攻击对光伏发电及火力发电混合电力系统带来的负荷频率失稳影响,首先,设计网络化电力负荷频率控制系统模型,并提出了一种带有缓存的模型预测控制方法;其次,考虑DoS攻击方能量受限情况,设计具有约束的DoS攻击模型;再次,通过设计的最优目标成本函数,构造增广矩阵,利用基于DoS攻击模态的分类讨论方法对火电-光伏电力系统进行渐进稳定性分析;最后,仿真结果表明,相较于之前负荷频率控制方法,所提出的模型预测控制可以有效减少DoS攻击对系统负荷频率异常波动的影响。

工业信息物理系统间隙DoS攻击的自适应事件触发稳定控制

针对具有间隙拒绝服务(DoS)攻击的工业信息物理系统(ICPS),研究一种基于观测器的自适应事件触发稳定控制方案。首先构造一个观测器估计系统状态,并采用一种新的自适应事件触发通信方案以节约网络资源;然后通过受频率和持续时间限制的间隙DoS攻击对ICPS的影响建立攻击模型,将受到DoS攻击的ICPS划分为DoS攻击活跃期系统和DoS攻击休眠期系统,并用一些过去成功触发状态重构当前受到攻击的状态,得到基于动态估计器的攻击补偿机制,保证系统在DoS攻击活跃期间的渐进稳定;最后运用分段Lyapunov泛函方法、Jensen’s不等式和Schur补引理,得到系统的稳定条件,并提出自适应事件触发参数、观测器增益和控制器增益的协同设计方案。用间歇反应器系统模拟间隙DoS攻击下基于自适应事件触发的ICPS系统稳定性能。仿真结果表明,自适应事件触发策略和基于动态估计器的补偿机制不仅能够提升系统的稳定性,还能有效降低数据传输的次数,系统在静态事件触发策略下的触发次数大约是在自适应事件触发策略下的4.5倍。

DoS攻击下多智能体系统无模型自适应迭代学习跟踪控制

针对周期性拒绝服务(DoS)攻击下多智能体系统有限时间趋同跟踪控制问题,本文提出了一种无模型自适应迭代学习控制(MFAILC)算法.假设多智能体系统具有固定拓扑结构,并且仅有部分智能体可获取到期望轨迹信息.在多智能体系统数据传输过程中,需要经由对数量化器进行量化处理.首先,使用伪偏导数将智能体系统动态线性化,处理过程中考虑符合伯努利分布的周期性DoS攻击现象,在此基础上设计了MFAILC控制算法,其次,采用压缩映射方法给出了一个在期望意义下保证跟踪误差收敛的充分条件,并在理论上证明了所提算法的收敛性.所提算法只需利用系统的输入输出数据就可完成趋同跟踪任务.最后,仿真结果验证了所提算法的有效性.

双通道受周期性DoS攻击下的NCSs事件触发控制

本文针对双通道(传感器-控制器通道、控制器-执行器通道)受到已知周期性拒绝服务(DoS)攻击的网络控制系统,设计了一种基于观测器的具有弹性事件触发策略的控制器。使用切换系统的方法,将受到周期性DoS攻击的网络控制系统分为DoS攻击休眠子系统和DoS攻击活跃子系统,在周期性DoS攻击活跃期,采用保持输入补偿策略以避免DoS攻击对传输数据的影响。通过引入两个辅助函数得到闭环增广系统,基于系统全局指数稳定的充分条件,通过给定的周期性DoS攻击参数,设计系统的状态观测器和事件触发控制器。仿真结果表明,所设计的控制器在保证系统稳定的同时,还能减少不必要的数据传输。

SGuard：A Lightweight SDN Safe-Guard Architecture for DoS Attacks

Software Defined Networking(SDN) is a revolutionary networking paradigm towards the future network,experiencing rapid development nowadays.However,its main characteristic,the separation of control plane and data plane,also brings about new security challenges,i.e.,Denial-of-Service(DoS) attacks specific to Open Flow SDN networks to exhaust the control plane bandwidth and overload the buffer memory of Open Flow switch.To mitigate the DoS attacks in the Open Flow networks,we design and implement SGuard,a security application on top of the NOX controller that mainly contains two modules:Access control module and Classification module.We employ novel six-tuple as feature vector to classify traffic flows,meanwhile optimizing classification by feature ranking and selecting algorithms.All the modules will cooperate with each other to complete a series of tasks such as authorization,classification and so on.At the end of this paper,we experimentally use Mininet to evaluate SGuard in a software environment.The results show that SGuard works efficiently and accurately without adding more overhead to the SDN networks.

Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.

Sliding Mode Control for Nonlinear Markovian Jump Systems Under Denial-of-Service Attacks

This paper investigates the sliding mode control(SMC) problem for a class of discrete-time nonlinear networked Markovian jump systems(MJSs) in the presence of probabilistic denial-of-service(Do S) attacks. The communication network via which the data is propagated is unsafe and the malicious adversary can attack the system during state feedback. By considering random Denial-of-Service attacks, a new sliding mode variable is designed, which takes into account the distribution information of the probabilistic attacks. Then, by resorting to Lyapunov theory and stochastic analysis methods, sufficient conditions are established for the existence of the desired sliding mode controller, guaranteeing both reachability of the designed sliding surface and stability of the resulting sliding motion.Finally, a simulation example is given to demonstrate the effectiveness of the proposed sliding mode control algorithm.

Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

In this paper,a new filtering fusion problem is studied for nonlinear cyber-physical systems under errorvariance constraints and denial-of-service attacks.To prevent data collision and reduce communication cost,the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors.Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identicallydistributed random variables.From the defenders’view,the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence.At the local filtering stage,a set of variance-constrained local filters are designed where the upper bounds(on the filtering error covariances)are first acquired and later minimized by appropriately designing filter parameters.At the fusion stage,all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule.Furthermore,the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance.A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.

A Novel Distributed LDoS Attack Scheme against Internet Routing

LDoS （Low-rate Denial of Service） attack, exploiting the flaws in the congestion avoidance mechanism of TCP protocol,is periodic, stealthy, and with high efficiency. Since BGP uses TCP as a transport protocol, it is subject to LDoS attacks as well. LDoS attacks can cause table reset, route flapping of BGP protocol. A deliberately constructed distributed low-rate DOS attacks can even generate surge of updates throughout the Internet. In this paper, we investigate the promotion of attack efficiency of this novel attack, and then propose an attack model to simulate the LDoS attack. Experiments prove that this attack model can exponentially lower the attack costs and improve the attack effect.

Decentralized Resilient H_∞Load Frequency Control for Cyber-Physical Power Systems Under DoS Attacks

This paper designs a decentralized resilient H_(∞)load frequency control(LFC)scheme for multi-area cyber-physical power systems(CPPSs).Under the network-based control framework,the sampled measurements are transmitted through the communication networks,which may be attacked by energylimited denial-of-service(DoS)attacks with a characterization of the maximum count of continuous data losses(resilience index).Each area is controlled in a decentralized mode,and the impacts on one area from other areas via their interconnections are regarded as the additional load disturbance of this area.Then,the closed-loop LFC system of each area under DoS attacks is modeled as an aperiodic sampled-data control system with external disturbances.Under this modeling,a decentralized resilient H_(∞)scheme is presented to design the state-feedback controllers with guaranteed H∞performance and resilience index based on a novel transmission interval-dependent loop functional method.When given the controllers,the proposed scheme can obtain a less conservative H_(∞)performance and resilience index that the LFC system can tolerate.The effectiveness of the proposed LFC scheme is evaluated on a one-area CPPS and two three-area CPPSs under DoS attacks.