一种基于软件定义网络的通信网络保护方法

为了应对互联网数据传输过程中的分布式拒绝服务攻击(DDoS),提高互联网数据传输的安全性,通过对SYN泛洪 攻击机理进行分析研究,提出应对SYN泛洪攻击较为有效的软件定义网络(SDN)防御机制,分析其防御机理,建立 SDN信息熵计算模型,并构建了实际仿真计算实验环境, 研究结果证明SDN能够较为快速有效的对SYN泛洪攻击进行防御,同时能够对类似于SYN攻击的其他DDoS攻击进行有效防御。     

ISP防御DDoS攻击机制研究

通过对DDoS攻击的原理及特征进行分析,并根据ISP在网络中的地位和作用,讨论了DDoS攻击的监测和防御方法,提出了建立分布式防御系统来应对DDoS攻击的方法。     

基于云环境的反射型DDoS攻击检测

随着软件定义网络、网络功能虚拟化、人工智能等技术的演进发展,云环境部署与应用日趋成熟.分布式拒绝服务(Distributed Denial of Service,DDoS)攻击的新变种反射型DDoS因低成本、难追踪等特点得到快速泛滥,云环境中的主机和应用服务面临着反射型DDoS攻击威胁.如何检测反射型DDoS攻击行为及...     

基于IXP 1200平台的DDoS攻击防御研究

分布式拒绝服务攻击（DDoS）已经成为互联网最大的威胁之一.提出了一种基于Intel IXP1200网络处理器平台的DDoS防御系统的设计方案,并实际实现了一个防御系统D-Fighter.提出了解决DDoS攻击的两个关键技术：数据包认证和细微流量控制的原理和方法,并在D-Fighter中设计实现.经过实际网络测试环境的应用测试表明,D-Fighter达到了设计目标,对DDoS攻击的防御有较好的效果.     

一种基于资源感知的小流量DDoS攻击防御方法

分布式拒绝服务攻击（DDoS）对网络具有极大的破坏性,严重影响现网的正常运营。虽然现网已经部署针对DDoS的流量清洗系统,然而小流量的攻击较洪水型攻击更难以被感知,进而不能得到有效的清洗。本文分析了网络中小流量DDoS攻击的原理和防御现状,并提出一种基于资源感知的小流量DDoS攻击防御方法。     

基于边界网关的自适应DDoS攻击防御策略

传统基于主机的防御无法应对分布式拒绝服务(DDoS)攻击对整个自治系统的冲击.提出了双过滤并行净化网络方案、基于自治系统的自适应概率包标记方案和基于源地址验证的单播反向路径转发策略,形成了基于边界网关的DDoS攻击防御体系,提高了包标记方案的效率,简化了防御部署.实验验证了该防御体系的有效性.     

DDoS攻击原理及其防御机制的研究

DDoS攻击是一种被黑客广泛应用的攻击方式,它以破坏计算机系统或网络的可用性为目标,危害性极大。本文首先介绍了DDoS攻击的攻击原理,接着从DDoS攻击的攻击手段和攻击方式两个方面对DoS攻击进行分类介绍,然后针对DDoS攻击的方式,提出了一种检测和防御DDoS攻击的模型,最后利用入侵检测技术和数据包过滤技术,设计了一个针对DDoS攻击的检测与防御系统,该系统具有配置简单、易于扩展、实用性较强等优点。     

浅析分布式拒绝服务的攻击步骤与防范策略

文中阐述拒绝服务和分布式拒绝服务的定义,分析了DDoS的攻击原理和攻击过程,针对通过网络连接以及利用合理的服务请求来占用过多资源,从而使合法用户无法得到服务的攻击给出了检测措施,然后结合实践经验与国内网络安全的现况,从保护主机系统、优化网络和路由结构、与ISP合作、扫描工具、入侵检测系统等方面给出防御DDoS攻击的有效措施。     

分布式拒绝服务攻击及防御机制研究

分布式拒绝服务(DDoS)攻击是当今网络面临的最严峻的威胁之一,研究防御DDoS攻击的技术非常重要。文章分析了DDoS攻击原理,并深入介绍了当前DDoS攻击的防御技术。     

网络蠕虫型分布式拒绝服务攻击的原理及防御

网络蠕虫型分布式拒绝服务攻击在扫描和捕获傀儡机时采用了网络蠕虫软件。通过这种传播形式布置DDoS的傀儡机具有数量更大、分布面更广、攻击时流量巨大的特点，阻塞被攻击目标同时还可造成大规模网络瘫痪，产生比普通DDoS攻击更加严重的后果。基于当前DDoS的最新研究成果，对网络蠕虫型分布式拒绝服务攻击的传播形式，攻击原理做出分析并提出了相应的防御方法。     

一种新的分布式DDoS攻击防御体系

Internet技术的发展和应用,给人们的生产和生活带来了很多便捷,但随之出现的网络安全问题,也成为日益严重的社会问题。针对网络中存在的DDoS攻击进行研究,以分布式并行系统的思想为基础,建立了一种新型DDoS攻击的安全防御体系。该体系通过不同组件间的相互协调、合作,实现了对DDoS攻击的分析及其防御。在对DDoS的攻击流量进行分析的过程中,以数据挖掘的模糊关联规则的方法进行分析,并实现了对攻击源的定位,有效地避免了攻击造成进一步的危害。     

基于蜜网技术的DDOS攻击防御策略

DDoS攻击的防御是当前网络安全研究领域中的难点。文章在分析DDoS攻击原理的基础上,介绍了蜜网这一主动防御技术的实现原理及功能,提出了基于蜜网防御DDOS攻击的模型并进行了设计和实现。     

Adaptive Defense Against Various Network Attacks

In defending against various network attacks, such as distributed denial-of-service (DDoS) attacks or worm attacks, a defense system needs to deal with various network conditions and dynamically changing attacks. Therefore, a good defense system needs to have a built-in “adaptive defense” functionality based on cost minimization—adaptively adjusting its configurations according to the network condition and attack severity in order to minimize the combined cost introduced by false positives (misidentify normal traffic as attack) and false negatives (misidentify attack traffic as normal) at any time. In this way, the adaptive defense system can generate fewer false alarms in normal situations or under light attacks with relaxed defense configurations, while protecting a network or a server more vigorously under severe attacks. In this paper, we present concrete adaptive defense system designs for defending against two major network attacks: SYN flood DDoS attack and Internet worm infection. The adaptive defense is a high-level system design that can be built on various underlying nonadaptive detection and filtering algorithms, which makes it applicable for a wide range of security defenses.     

Detection and defense of DDoS attack–based on deep learning in OpenFlow‐based SDN

Distributed denial of service (DDoS) is a special form of denial of service attack. In this paper, a DDoS detection model and defense system based on deep learning in Software‐Defined Network (SDN) environment are introduced. The model can learn patterns from sequences of network traffic and trace network attack activities in a historical manner. By using the defense system based on the model, the DDoS attack traffic can be effectively cleaned in Software‐Defined Network. The experimental results demonstrate the much better performance of our model compared with conventional machine learning ways. It also reduces the degree of dependence on environment, simplifies the real‐time update of detection system, and decreases the difficulty of upgrading or changing detection strategy.     

DDoS攻击恶意行为知识库构建

针对分布式拒绝服务（distributed denial of service,DDoS）网络攻击知识库研究不足的问题,提出了DDoS攻击恶意行为知识库的构建方法。该知识库基于知识图谱构建,包含恶意流量检测库和网络安全知识库两部分：恶意流量检测库对 DDoS 攻击引发的恶意流量进行检测并分类;网络安全知识库从流量特征和攻击框架对DDoS 攻击恶意行为建模,并对恶意行为进行推理、溯源和反馈。在此基础上基于DDoS 开放威胁信号（DDoS open threat signaling,DOTS）协议搭建分布式知识库,实现分布式节点间的数据传输、DDoS攻击防御与恶意流量缓解功能。实验结果表明,DDoS攻击恶意行为知识库能在多个网关处有效检测和缓解DDoS攻击引发的恶意流量,并具备分布式知识库间的知识更新和推理功能,表现出良好的可扩展性。     

DDoS attack detection and defense based on hybrid deep learning model in SDN

Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.     

基于流媒体服务DDoS攻击防范研究

分布式拒绝服务（Distributed Deny of Service,DDoS）攻击是目前最难解决的网络安全问题之一。在研究RTSP（Real-Time Streaming Protocol）协议漏洞基础上,提出一种有效防御流媒体服务DDoS攻击防御方案。该方案基于时间方差图法（Variance-TimePlots,VTP）,计算自相似参数Hurst值,利用正常网络流量符合自相似模型的特性来进行DDoS攻击检测,并综合采用黑白名单技术对流量进行处理。最后通过MATLAB仿真工具进行了模拟实验,并对结果进行了分析,在协议分析基础上能合理控制流量,使得DDoS攻击检测准确率、实时性高,目标流媒体服务器带宽和资源得到了有效保护。     

Intrusion Detection Routers: Design, Implementation and Evaluation Using an Experimental Testbed

In this paper, we present the design, the implementation details, and the evaluation results of an intrusion detection and defense system for distributed denial-of-service (DDoS) attack. The evaluation is conducted using an experimental testbed. The system, known as intrusion detection router (IDR), is deployed on network routers to perform online detection on any DDoS attack event, and then react with defense mechanisms to mitigate the attack. The testbed is built up by a cluster of sufficient number of Linux machines to mimic a portion of the Internet. Using the testbed, we conduct real experiments to evaluate the IDR system and demonstrate that IDR is effective in protecting the network from various DDoS attacks.