低速率拒绝服务LDoS(Low-rate Denial of Service)攻击是当代大数据中心和云计算平台的最大威胁之一。本文主要通过NS2仿真平台实现LDoS攻击,并利用TCP状态机模型建立HMM模型,计算TCP状态机拥塞控制四个参量求加权平均数,得到的值用NCPS...低速率拒绝服务LDoS(Low-rate Denial of Service)攻击是当代大数据中心和云计算平台的最大威胁之一。本文主要通过NS2仿真平台实现LDoS攻击,并利用TCP状态机模型建立HMM模型,计算TCP状态机拥塞控制四个参量求加权平均数,得到的值用NCPSD的值代替,作为判别有无攻击的依据,以此达到检测LDoS攻击的目的。展开更多
针对自适应拥塞控制系统操作特性所出现的低速率拒绝服务攻击(LDoS,Low-rate Denial of Service attacks)是近年来的一类新型DoS攻击。与传统洪范式DoS攻击相比,LDoS具有攻击效率更高、检测难度更大等特点。在对常用攻击模拟分析平台NS...针对自适应拥塞控制系统操作特性所出现的低速率拒绝服务攻击(LDoS,Low-rate Denial of Service attacks)是近年来的一类新型DoS攻击。与传统洪范式DoS攻击相比,LDoS具有攻击效率更高、检测难度更大等特点。在对常用攻击模拟分析平台NS2进行缺陷分析的基础上,提出了一种基于有色Petri网(CPN)的LDoS攻击系统建模方法,应用仿真工具CPN Tools实现了对目标系统行为及LDoS攻击效果的仿真,并在此基础上提出了一种基于自适应资源投放的系统防范方案,仿真结果表明此方案能够有效降低LDoS攻击对目标系统服务质量的影响。展开更多
针对随机早期检测(random early detection,RED)算法在慢速拒绝服务攻击(low-rate deny of serv-ice,LDoS)面前的脆弱性问题,本文通过对比路由器分别在RED和尾丢弃Drop-Tail算法管理下遭受LDoS攻击时的队列平均占用率及吞吐量,指出虽然...针对随机早期检测(random early detection,RED)算法在慢速拒绝服务攻击(low-rate deny of serv-ice,LDoS)面前的脆弱性问题,本文通过对比路由器分别在RED和尾丢弃Drop-Tail算法管理下遭受LDoS攻击时的队列平均占用率及吞吐量,指出虽然路由器在RED算法下具有较大的空闲缓冲区,却不能对网络流量攻击起到缓冲作用.仿真对比实验表明,LDoS攻击使得路由器在RED下比Drop-Tail具有更大的链路损失带宽.指出现有LDoS的防范和检测方法的不足,构造了一种分布式LDoS攻击模型并给出一组模型实例,该模型说明现有突发流量检测方法不足以弥补RED脆弱性,也说明网络流量行为的关联复杂性.展开更多
为提高低压差线性稳压器(Low-DropOut Linear Regulator,LDO)的稳定性并降低前馈电路所产生的噪声,提出了一种生成自适应补偿零点的低噪声前馈电路。该前馈电路通过镜像调整管的负载电流,通过低值反馈电阻形成高增益反馈信号,与LDO输出...为提高低压差线性稳压器(Low-DropOut Linear Regulator,LDO)的稳定性并降低前馈电路所产生的噪声,提出了一种生成自适应补偿零点的低噪声前馈电路。该前馈电路通过镜像调整管的负载电流,通过低值反馈电阻形成高增益反馈信号,与LDO输出电压经反馈网络传递给反馈端的信号耦合形成由负载电容、负载电流控制的可控零点,可有效提高LDO电路整体的稳定性。此外,电路内部加入了产生动态极点的自适应电流补偿电路以保证次极点不会对环路的相位裕度产生影响。基于0.18μm BCD工艺设计,该电路在0~800 mA的宽负载范围、5 V输入3.3 V输出下相位裕度均高于48°,适用负载电容范围≥1μF,同时该LDO在10~100 kHz的频率范围内输出噪声仅为5.0617μVrms。展开更多
Cybersecurity has always been the focus of Internet research.An LDoS attack is an intelligent type of DoS attack,which reduces the quality of network service by periodically sending high-speed but short-pulse attack t...Cybersecurity has always been the focus of Internet research.An LDoS attack is an intelligent type of DoS attack,which reduces the quality of network service by periodically sending high-speed but short-pulse attack traffic.Because of its concealment and low average rate,the traditional DoS attack detection methods are challenging to be effective.The existing LDoS attack detection methods generally have the problems of high FPR and FNR.A cloud model-based LDoS attack detection method is proposed,and a classifier based on SVM is used to train and classify the feature parameters.The detection method is verified and tested in the NS2 simulation platform and Test-bed network environment.Compared with the existing research results,the proposed method requires fewer samples,and it has lower FPR and FNR.展开更多
文摘低速率拒绝服务LDoS(Low-rate Denial of Service)攻击是当代大数据中心和云计算平台的最大威胁之一。本文主要通过NS2仿真平台实现LDoS攻击,并利用TCP状态机模型建立HMM模型,计算TCP状态机拥塞控制四个参量求加权平均数,得到的值用NCPSD的值代替,作为判别有无攻击的依据,以此达到检测LDoS攻击的目的。
文摘针对自适应拥塞控制系统操作特性所出现的低速率拒绝服务攻击(LDoS,Low-rate Denial of Service attacks)是近年来的一类新型DoS攻击。与传统洪范式DoS攻击相比,LDoS具有攻击效率更高、检测难度更大等特点。在对常用攻击模拟分析平台NS2进行缺陷分析的基础上,提出了一种基于有色Petri网(CPN)的LDoS攻击系统建模方法,应用仿真工具CPN Tools实现了对目标系统行为及LDoS攻击效果的仿真,并在此基础上提出了一种基于自适应资源投放的系统防范方案,仿真结果表明此方案能够有效降低LDoS攻击对目标系统服务质量的影响。
文摘针对随机早期检测(random early detection,RED)算法在慢速拒绝服务攻击(low-rate deny of serv-ice,LDoS)面前的脆弱性问题,本文通过对比路由器分别在RED和尾丢弃Drop-Tail算法管理下遭受LDoS攻击时的队列平均占用率及吞吐量,指出虽然路由器在RED算法下具有较大的空闲缓冲区,却不能对网络流量攻击起到缓冲作用.仿真对比实验表明,LDoS攻击使得路由器在RED下比Drop-Tail具有更大的链路损失带宽.指出现有LDoS的防范和检测方法的不足,构造了一种分布式LDoS攻击模型并给出一组模型实例,该模型说明现有突发流量检测方法不足以弥补RED脆弱性,也说明网络流量行为的关联复杂性.
基金supported by the National Natural Science Foundation of China (Grant Nos.61772189,61772191)the Hunan Provincial Natural Science Foundation of China (2019JJ40037).
文摘Cybersecurity has always been the focus of Internet research.An LDoS attack is an intelligent type of DoS attack,which reduces the quality of network service by periodically sending high-speed but short-pulse attack traffic.Because of its concealment and low average rate,the traditional DoS attack detection methods are challenging to be effective.The existing LDoS attack detection methods generally have the problems of high FPR and FNR.A cloud model-based LDoS attack detection method is proposed,and a classifier based on SVM is used to train and classify the feature parameters.The detection method is verified and tested in the NS2 simulation platform and Test-bed network environment.Compared with the existing research results,the proposed method requires fewer samples,and it has lower FPR and FNR.
