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.

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.

Periodic event-triggered secure consensus for networked mechanical systems under Denial-of-Service attacks

This paper concentrates on the secure consensus problem of networked mechanical/Euler–Lagrange systems.First,a new periodic event-triggered(PET)secure distributed observer is proposed to estimate the leader information.The proposed distributed observer only relies on the PET data from its neighbors,which can significantly reduce the communication and computational burden.More importantly,it is secure in the sense that it can work normally regardless of the Denial-of-Service(DoS)attacks.Second,based on the proposed distributed observer,an adaptive fuzzy control law is proposed for each Euler–Lagrange system.A PET mechanism is integrated into the controller,which can reduce the control update.This is helpful for both energy saving and fault tolerance of actuators.Moreover,the PET mechanism naturally makes the controller easy to be implemented in digital platform.The property of fuzzy logic systems and Gronwall inequality are skillfully utilized to show the stability of the closed-loop system.Finally,the proposed control scheme is verified on real Euler–Lagrange systems,which contain a robot manipulator and several servo motors.

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:the optimality of attack strategy

With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.

A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection attacks.Most DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing resources.This paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to victims.It defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection attacks.BLCD also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS packets.For evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence locations.Experiments show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

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.

一种基于路由器矢量边采样的IP追踪技术

提出了一种新型的边采样方法"路由器矢量边采样"(RVES),使得概率包标记(probability packet marking,简称PPM)设备容易实现和部署.在图论模型上,RVES以网络接口替代路由器作为顶点,以路由器"矢量边"替代传统采样边.该方法实施简单,标记概率的策略配置灵活,可以有效解决分布式拒绝服务(router's vector-edge-sampling,简称DDoS)攻击的重构问题.基于传统边采样的PPM相关技术依然适用于RVES方法.原理样机已经研制出并部署在Internet上.实验结果验证了该方法的有效性和可行性.

A Resilient Control Strategy for Cyber-Physical Systems Subject to Denial of Service Attacks:A Leader-Follower Set-Theoretic Approach

Multi-agent systems are usually equipped with open communication infrastructures to improve interactions efficiency,reliability and sustainability.Although technologically costeffective,this makes them vulnerable to cyber-attacks with potentially catastrophic consequences.To this end,we present a novel control architecture capable to deal with the distributed constrained regulation problem in the presence of time-delay attacks on the agents’communication infrastructure.The basic idea consists of orchestrating the interconnected cyber-physical system as a leader-follower configuration so that adequate control actions are computed to isolate the attacked unit before it compromises the system operations.Simulations on a multi-area power system confirm that the proposed control scheme can reconfigure the leader-follower structure in response to denial ofservice(DoS)attacks.

Secure Synchronization Control for a Class of Cyber-Physical Systems With Unknown Dynamics

This paper investigates the secure synchronization control problem for a class of cyber-physical systems(CPSs)with unknown system matrices and intermittent denial-of-service(DoS)attacks.For the attack free case,an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization,and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation(ARE).For considering the attack cases,it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system matrices.In order to overcome this difficulty,a matrix polynomial replacement method is given and it is shown that,the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks hold.Finally,two examples are given to illustrate the effectiveness of the proposed approaches.

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.

Security Control for Uncertain Networked Control Systems under DoS Attacks and Fading Channels

This paper characterizes the joint effects of plant uncertainty,Denial-of-Service(DoS)attacks,and fading channel on the stabilization problem of networked control systems(NCSs).It is assumed that the controller remotely controls the plant and the control input is transmitted over a fading channel.Meanwhile,considering the sustained attack cycle and frequency of DoS attacks are random,the packet-loss caused by DoS attacks is modelled by a Markov process.The sampled-data NCS is transformed into a stochastic form with Markov jump and uncertain parameter.Then,based on Lyapunov functional method,linear matrix inequality(LMI)-based sufficient conditions are presented to ensure the stability of uncertain NCSs.The main contribution of this article lies in the construction of NCSs based on DoS attacks into Markov jump system(MJS)and the joint consideration of fading channel and plant uncertainty.

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.

Enhanced Timestamp Discrepancy to Limit Impact of Replay Attacks in MANETs

Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.

Vulnerabilities of LDAP As An Authentication Service

Lightweight Directory Access Protocol (LDAP) servers are widely used to authenticate users in enterprise level networks. Organizations such as universities and small to medium-sized businesses use LDAP for a variety of applications including e-mail clients, SSH, and workstation authentication. Since many organizations build dependencies on the LDAP service, a Denial-of-Service (DoS) attack to the service can cause a greater number of services disrupted. This paper examines the danger in the use of LDAP for user authentication by executing a DoS attack exploiting the TCP three-way handshake required when initializing a connection to an LDAP server.

An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

The success of Internet of Things(IoT)deployment has emerged important smart applications.These applications are running independently on different platforms,almost everywhere in the world.Internet of Medical Things(IoMT),also referred as the healthcare Internet of Things,is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems.Indeed,with the impact of the COVID-19 pandemic,a large number of interconnected devices designed to create smart networks.These networks monitor patients from remote locations as well as tracking medication orders.However,IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks.In this paper,we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes.For this purpose,we develop a new algorithm for Intrusion Detection System(IDS)to detect malicious activities in the Internet of Medical Things.The proposed scheme minimizes as possible the number of attacks to ensure data security,and preserve confidentiality of gathered data.In order to check the viability of our approach,we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks.

基于状态预测器和事件触发的多无人系统周期性DoS攻击韧性一致性控制

This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average(ARIMA)agent state prediction mechanism against periodic denial-of-service(Do S)attacks.The state predictor is used to predict the state of neighbor agents during periodic Do S attacks and maintain consistent control of multiple unmanned systems under Do S attacks.Considering the existing prediction error between the actual state and the predicted state,the estimated error is regarded as the uncertainty system disturbance,which is dealt with by the designed disturbance observer.The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term.Furthermore,this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic Do S attacks and reduce the frequency of actuator output changes.It is proved that the Zeno behavior can be excluded.Finally,the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations.

Event-triggered bipartite consensus over cooperation-competition networks under DoS attacks

This paper addresses the bipartite consensus over cooperation-competition networks affected by denial-of-service(DoS) attacks.Consider that a network consists of multiple interactive agents, and the relationship between neighboring agents is cooperative or competitive. Meanwhile, information transmission among the agents is unavailable during the intervals of attacks. In order to save communication resources and exclude the Zeno behavior, an event-triggered scheme depending on the sampled-data information from neighboring agents is proposed, and efficient defense strategies in response to the attacks are put forward.Suppose that the frequency and duration of DoS attacks meet certain requirements, then according to the signed graph theory,the La Salle’s invariance principle, and the convergence of monotone sequences, the results of bipartite consensus via the eventtriggered protocol are provided, which are mainly related to the communication topology of the network, the sampling period,and the threshold parameters in the event-triggered scheme. It is shown that the bipartite consensus is realized even though the DoS attacks take place frequently. Furthermore, this paper discusses the bipartite consensus in the presence of DoS attacks with a random unsuccessful rate. Finally, numerical simulations illustrate the theoretical results.

H_(∞)/Passive Synchronization of Semi-Markov Jump Neural Networks Subject to Hybrid Attacks via an Activation Function Division Approach

In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks,and they are described by two groups of independent Bernoulli distributions.On this foundation,via the Lyapunov stability theory and linear matrix inequality technology,the H_(∞)/passive-based performance criteria for semi-Markov jump neural networks are obtained.Additionally,an activation function division approach for neural networks is adopted to further reduce the conservatism of the criteria.Finally,a simulation example is provided to verify the validity and feasibility of the proposed method.