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
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.
