网络流量分类在网络管理和安全中至关重要,尤其是精准识别分布式拒绝服务(Distributed Denial of Service,DDoS)攻击这一威胁。DDoS攻击会导致服务中断、资源耗尽和经济损失,严重影响服务质量(QoS)。尽管集中式模型在DDoS攻击检测中取...网络流量分类在网络管理和安全中至关重要,尤其是精准识别分布式拒绝服务(Distributed Denial of Service,DDoS)攻击这一威胁。DDoS攻击会导致服务中断、资源耗尽和经济损失,严重影响服务质量(QoS)。尽管集中式模型在DDoS攻击检测中取得了一定成效,但在实际应用中存在挑战:数据分布不均、数据集中传输困难,以及异构设备和动态网络环境的限制,从而难以实现实时检测。为应对这些问题,本文提出了一种基于异步个性化联邦学习的DDoS攻击检测与缓解方法AdaPerFed(Adaptive Personalized Federated Learning)。首先,通过定制的ResNet架构高效处理一维流量数据,并集成Net模块增强特征提取能力。然后,通过软件定义网络(SDN,Software-Defined Networking)模拟复杂动态网络环境,并引入完善的缓解系统应对多样化攻击场景。个性化联邦学习框架有效处理了非独立同分布(Non-IID,Non-Independent and Identically Distributed)数据问题,并通过异步学习机制适应异构设备和网络条件的差异,提升了系统的鲁棒性和扩展性。实验结果表明,AdaPerFed在CICDDoS2019、CIC-IDS2017和InSDN等数据集上均优于其他联邦学习算法,在不同客户端数量下展现出更快的收敛速度和更强的鲁棒性,DDoS检测准确率提升了15%~20%。消融实验进一步验证了个性化聚合模块对系统性能的显著提升。展开更多
物联网(Internet of Things,IoT)技术的发展给工业界和日常生活带来便利的同时,海量易受到各种攻击和破坏的IoT设备也降低了分布式拒绝服务(Distributed Denial of Service,DDoS)攻击发起的成本,使被攻击方无法响应正常用户访问.为了在...物联网(Internet of Things,IoT)技术的发展给工业界和日常生活带来便利的同时,海量易受到各种攻击和破坏的IoT设备也降低了分布式拒绝服务(Distributed Denial of Service,DDoS)攻击发起的成本,使被攻击方无法响应正常用户访问.为了在物联网边缘中快速、准确地完成DDoS攻击检测,弥补现有方法资源开销大、不精确的缺陷,本文提出了一种基于轻量化卷积神经网络(Lightweight Convolutional Neural Networks,LCNN)的DDoS检测方法.面向物联网流量特性,方法首先提取包级特征和经冗余分析筛选得到的流级特征.之后设计了低参数和运算量的卷积神经网络LCNN,最后基于变维后的特征,快速检测定位攻击.实验结果表明,方法检测准确率达99.4%.同时LCNN在FPGA中能够以较少的资源消耗,保证在1ms时间内完成对一条流的推理判断.展开更多
软件定义网络(Software Defined Network,SDN)通过控制平面和数据平面的解耦实现了网络的集中控制和灵活调度,但是这种架构设计也给可靠性、负载均衡和安全性等方面带来了挑战.其中,针对SDN环境中的分布式拒绝服务攻击(Distributed Deni...软件定义网络(Software Defined Network,SDN)通过控制平面和数据平面的解耦实现了网络的集中控制和灵活调度,但是这种架构设计也给可靠性、负载均衡和安全性等方面带来了挑战.其中,针对SDN环境中的分布式拒绝服务攻击(Distributed Denial of Service,DDoS),本文提出了一种结合门控循环单元(Gated Recurrent Unit,GRU)和注意力机制的DDoS攻击检测与缓解模型.相较于近期众多先进的DDoS攻击检测方法,本研究所提出的模型在检测性能上表现出了优越性,在数据集InSDN、CICIDS2018和CICDDoS2019上的检测准确率达到了100%、100%和99.62%.同时,为了进一步验证模型的有效性,本文在基于Mininet的SDN模拟环境中模拟DDoS攻击场景并对模型的缓解模块进行了检验.实验结果显示,该模型的缓解模块能够在检测到攻击后迅速采取有效的防御措施,显著减轻DDoS攻击对网络造成的影响.展开更多
In this paper,we investigate the periodic traveling wave solutions problem for a single population model with advection and distributed delay.By the bifurcation analysis method,we can obtain periodic traveling wave so...In this paper,we investigate the periodic traveling wave solutions problem for a single population model with advection and distributed delay.By the bifurcation analysis method,we can obtain periodic traveling wave solutions for this model under the influence of advection term and distributed delay.The obtained results indicate that weak kernel and strong kernel can both deduce the existence of periodic traveling wave solutions.Finally,we apply the main results in this paper to Logistic model and Nicholson’s blowflies model.展开更多
This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise co...This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising dur-ing measurements, thereby enhancing the robustness and stabi-lity of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference sig-nals utilizing local information and communication with neigh-bors. Subsequently, a fixed-time sliding mode controller is intro-duced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve dis-tributed average tracking of reference signals, and rigorous ana-lytical methods are employed to substantiate the fixed-time sta-bility. Finally, numerical simulation results are provided to vali-date the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.展开更多
This paper proposes a distributed event-triggered control(ETC)framework to address cooperative target fencing challenges in UAV swarm.The proposed architecture eliminates the reliance on preset formation parameters wh...This paper proposes a distributed event-triggered control(ETC)framework to address cooperative target fencing challenges in UAV swarm.The proposed architecture eliminates the reliance on preset formation parameters while achieving multi-objective cooperative control for target fencing,network connectivity preservation,collision avoidance,and communication efficiency optimization.Firstly,a differential state observer is constructed to obtain the target's unmeasurable states.Secondly,leveraging swarm selforganization principles,a geometric-constraint-free distributed fencing controller is designed by integrating potential field methods with consensus theory.The controller dynamically adjusts inter-UAV distances via single potential function,enabling coordinated optimization of persistent network connectivity and collision-free motion during target fencing.Thirdly,a dual-threshold ETC mechanism based on velocity consensus deviation and fencing error is proposed,which can be triggered based on task features to dynamically adjust the communication frequency,significantly reduce the communication burden and exclude Zeno behavior.Theoretical analysis demonstrates the stability of closed-loop systems.Multi-scenario simulations show that the proposed method can achieve robust fencing under target maneuverability,partial UAV failures,and communication disturbances.展开更多
分布式拒绝服务(Distributed Denial of Service,DDoS)攻击已经成为网络安全的主要威胁之一,其中应用层DDoS攻击是主要的攻击手段。应用层DDoS攻击是针对具体应用服务的攻击,其在网络层行为表现正常,传统安全设备无法有效抵御。同时,现...分布式拒绝服务(Distributed Denial of Service,DDoS)攻击已经成为网络安全的主要威胁之一,其中应用层DDoS攻击是主要的攻击手段。应用层DDoS攻击是针对具体应用服务的攻击,其在网络层行为表现正常,传统安全设备无法有效抵御。同时,现有的针对应用层DDoS攻击的检测方法检测能力不足,难以适应攻击模式的变化。为此,文章提出一种基于时空图神经网络(Spatio-Temporal Graph Neural Network,STGNN)的应用层DDoS攻击检测方法,利用应用层服务的特征,从应用层数据和应用层协议交互信息出发,引入注意力机制并结合多个GraphSAGE层,学习不同时间窗口下的实体交互模式,进而计算检测流量与正常流量的偏差,完成攻击检测。该方法仅利用时间、源IP、目的IP、通信频率、平均数据包大小5维数据便可有效识别应用层DDoS攻击。由实验结果可知,该方法在攻击样本数量较少的情况下,与对比方法相比可获得较高的Recall和F1分数。展开更多
文摘物联网(Internet of Things,IoT)技术的发展给工业界和日常生活带来便利的同时,海量易受到各种攻击和破坏的IoT设备也降低了分布式拒绝服务(Distributed Denial of Service,DDoS)攻击发起的成本,使被攻击方无法响应正常用户访问.为了在物联网边缘中快速、准确地完成DDoS攻击检测,弥补现有方法资源开销大、不精确的缺陷,本文提出了一种基于轻量化卷积神经网络(Lightweight Convolutional Neural Networks,LCNN)的DDoS检测方法.面向物联网流量特性,方法首先提取包级特征和经冗余分析筛选得到的流级特征.之后设计了低参数和运算量的卷积神经网络LCNN,最后基于变维后的特征,快速检测定位攻击.实验结果表明,方法检测准确率达99.4%.同时LCNN在FPGA中能够以较少的资源消耗,保证在1ms时间内完成对一条流的推理判断.
文摘软件定义网络(Software Defined Network,SDN)通过控制平面和数据平面的解耦实现了网络的集中控制和灵活调度,但是这种架构设计也给可靠性、负载均衡和安全性等方面带来了挑战.其中,针对SDN环境中的分布式拒绝服务攻击(Distributed Denial of Service,DDoS),本文提出了一种结合门控循环单元(Gated Recurrent Unit,GRU)和注意力机制的DDoS攻击检测与缓解模型.相较于近期众多先进的DDoS攻击检测方法,本研究所提出的模型在检测性能上表现出了优越性,在数据集InSDN、CICIDS2018和CICDDoS2019上的检测准确率达到了100%、100%和99.62%.同时,为了进一步验证模型的有效性,本文在基于Mininet的SDN模拟环境中模拟DDoS攻击场景并对模型的缓解模块进行了检验.实验结果显示,该模型的缓解模块能够在检测到攻击后迅速采取有效的防御措施,显著减轻DDoS攻击对网络造成的影响.
基金Supported by the National Natural Science Foundation of China(12261050)Science and Technology Project of Department of Education of Jiangxi Province(GJJ2201612 and GJJ211027)Natural Science Foundation of Jiangxi Province of China(20212BAB202021)。
文摘In this paper,we investigate the periodic traveling wave solutions problem for a single population model with advection and distributed delay.By the bifurcation analysis method,we can obtain periodic traveling wave solutions for this model under the influence of advection term and distributed delay.The obtained results indicate that weak kernel and strong kernel can both deduce the existence of periodic traveling wave solutions.Finally,we apply the main results in this paper to Logistic model and Nicholson’s blowflies model.
基金supported by the National Natural Science Foundation of China(61673130).
文摘This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising dur-ing measurements, thereby enhancing the robustness and stabi-lity of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference sig-nals utilizing local information and communication with neigh-bors. Subsequently, a fixed-time sliding mode controller is intro-duced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve dis-tributed average tracking of reference signals, and rigorous ana-lytical methods are employed to substantiate the fixed-time sta-bility. Finally, numerical simulation results are provided to vali-date the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.
文摘This paper proposes a distributed event-triggered control(ETC)framework to address cooperative target fencing challenges in UAV swarm.The proposed architecture eliminates the reliance on preset formation parameters while achieving multi-objective cooperative control for target fencing,network connectivity preservation,collision avoidance,and communication efficiency optimization.Firstly,a differential state observer is constructed to obtain the target's unmeasurable states.Secondly,leveraging swarm selforganization principles,a geometric-constraint-free distributed fencing controller is designed by integrating potential field methods with consensus theory.The controller dynamically adjusts inter-UAV distances via single potential function,enabling coordinated optimization of persistent network connectivity and collision-free motion during target fencing.Thirdly,a dual-threshold ETC mechanism based on velocity consensus deviation and fencing error is proposed,which can be triggered based on task features to dynamically adjust the communication frequency,significantly reduce the communication burden and exclude Zeno behavior.Theoretical analysis demonstrates the stability of closed-loop systems.Multi-scenario simulations show that the proposed method can achieve robust fencing under target maneuverability,partial UAV failures,and communication disturbances.
