一种针对基于OpenFlow的SDN网络中控制层面的DoS攻击研究

针对OpenFlow协议报文交换机制里所有非数据报文均需要通过PACKET_IN报文上传控制器的弱点,提出一种不停查询未知转发地址从而造成SDN网络控制层面资源耗尽的新型DoS攻击方式,同时基于SDN网络可编程性提出检测攻击与降低网络时延的解决策略。首先通过SDN控制器北向应用接口,使用Defense4ALL应用中自定义功能,针对DoS攻击特性检测网络中恶意流量。然后利用控制器动态配置特性,实时更新交换机配置文件,改变网络转发策略,从而减轻攻击对整个网络造成的影响。实验仿真表明,在大规模高速攻击中,该方法的检测成功率接近100%,在攻击源较少的慢速攻击中检测成功率低于80%,整体网络延迟降低10ms以上。所提出的解决策略可以有效减少针对控制平面的DoS攻击对整个网络的干扰。     

面向SDN拓扑发现的LDoS攻击防御技术研究

准确获取网络拓扑是软件定义网络（Software?Defined?Network,SDN）中控制器进行有效决策的前提,而现有拓扑发现机制难以有效应对低速率拒绝服务（Low rate Denial of Service,LDoS）攻击等行为。通过理论和实验分析LDoS攻击对SDN拓扑发现造成的影响,提出了一种面向SDN拓扑发现的LDoS攻击防御机制TopoGuard。TopoGuard根据LDoS攻击的周期性特征,通过连续突发检测快速发现存在的疑似攻击场景,并基于主动链路识别策略避免攻击行为造成网络拓扑中断。最后,在OpenDaylight控制器上实现了TopoGuard。实验结果显示,TopoGuard能够有效检测和防御LDoS攻击行为,保证控制器获取全局拓扑信息的正确性。     

软件定义网络环境下的低速率拒绝服务攻击检测方法

低速率拒绝服务（LDoS）攻击是一种拒绝服务（DoS）攻击改进形式,因其攻击平均速率低、隐蔽性强,使得检测LDoS攻击成为难点。针对上述难点,提出了一种在软件定义网络（SDN）的架构下,基于加权均值漂移-K均值算法（WMS-Kmeans）的LDoS攻击检测方法。首先,通过获取OpenFlow交换机的流表信息,分析并提取出SDN环境下LDoS攻击流量的六元组特征;然后,利用平均绝对值百分比误差作为均值漂移聚类中欧氏距离的权值,以此产生的簇心作为K-Means的初始中心对流表进行聚类,从而实现LDoS攻击的检测。实验结果表明：在SDN环境下,所提方法对LDoS攻击具有较好的检测性能,平均检测率达到99.29%,平均误警率和平均漏警率分别为1.97%和0.69%。     

SDN环境下的LDoS攻击检测与防御技术

低速率拒绝服务(LDoS)攻击是一种新型的网络攻击方式,其特点是攻击成本低,隐蔽性强。作为一种新型的网络架构,软件定义网络(SDN)同样面临着LDoS攻击的威胁。但SDN网络的控制与转发分离、网络行为可编程等特点又为LDoS攻击的检测和防御提供了新的思路。提出了一种基于OpenFlow协议的LDoS攻击检测和防御方法。通过对每条OpenFlow数据流的速率单独进行统计,并利用信号检测中的双滑动窗口法实现对攻击流量的检测,一旦检测到攻击流量,控制器便可以通过下发流表的方式实现对攻击行为的实时防御。实验表明,该方法能够有效检测出LDoS攻击,并能够在较短时间内实现对攻击行为的防御。     

SDN环境下基于DBN的DDoS攻击检测

软件定义网络(SDN)作为新型网络架构模式,其安全威胁主要来自DDoS攻击,建立高效的DDoS攻击检测系统是网络安全管理的重要内容.在SDN环境下,针对DDoS的入侵检测算法具有支持协议少、实用性差等缺陷,为此,提出一种基于深度信念网络(DBN)的DDoS攻击检测算法.分析SDN环境下DDoS攻击的机制,通过Mininet模拟SDN的网络拓扑结构,并使用Wireshark完成DDoS流量数据包的收集和检测.实验结果表明,与XGBoost、随机森林、支持向量机算法相比,该算法具有攻击检测准确性高、误报率低、检测速率快和易于扩展等优势,综合性能较好.     

面向软件定义网络架构的入侵检测模型设计与实现

针对传统入侵检测方法无法检测软件定义网络（SDN）架构的特有攻击行为的问题,设计一种基于卷积神经网络（CNN）的入侵检测模型。首先,基于SDN流表项设计了特征提取方法,通过采集SDN特有攻击样本形成攻击流表数据集;然后,采用CNN进行训练和检测,并针对SDN攻击样本量较小而导致的识别率低的问题,设计了一种基于概率的加强训练方法。实验结果表明,所提的入侵检测模型可以有效检测面向SDN架构的特有攻击,具有较高的准确率,所提的基于概率的加强学习方法能有效提升小概率攻击的识别率。     

基于SDN的DDoS攻击防御系统

软件定义网络（SDN）是一种新兴网络架构,通过将转发层和控制层分离,实现网络的集中管控。控制器作为SDN网络的核心,容易成为被攻击的目标,分布式拒绝服务（DDoS）攻击是SDN网络面临的最具威胁的攻击之一。针对这一问题,本文提出一种基于机器学习的DDoS攻击检测模型。首先基于信息熵监控交换机端口流量来判断是否存在异常流量,检测到异常后提取流量特征,使用SVM+K-Means的复合算法检测DDoS攻击,最后控制器下发丢弃流表处理攻击流量。实验结果表明,本文算法在误报率、检测率和准确率指标上均优于SVM算法和K-Means算法。     

基于可编程协议无关报文处理的分布式拒绝服务攻击检测

传统软件定义网络（SDN）中的分布式拒绝服务（DDoS）攻击检测方法需要控制平面与数据平面进行频繁通信,这会导致显著的开销和延迟,而目前可编程数据平面由于语法无法实现复杂检测算法,难以保证较高检测效率。针对上述问题,提出了一种基于可编程协议无关报文处理（P4）可编程数据平面的DDoS攻击检测方法。首先,利用基于P4改进的信息熵进行初检,判断是否有可疑流量发生;然后再利用P4提取特征只需微秒级时长的优势,提取可疑流量的六元组特征导入数据标准化—深度神经网络（data standardization-deep neural network,DS-DNN）复检模块,判断其是否为DDoS攻击流量;最后,模拟真实环境对该方法的各项评估指标进行测试。实验结果表明,该方法能够较好地检测SDN环境下的DDoS攻击,在保证较高检测率与准确率的同时,有效降低了误报率,并将检测时长缩短至毫秒级别。     

基于Markov的无线传感器网络入侵检测机制

本文采用Markov线性预测模型,为无线传感器网络设计了一种基于流量预测的拒绝服务攻击检测方案——MPDD。在该方案中,每个节点基于流量预测判断和检测异常网络流量,无需特殊的硬件支持和节点之间的合作;提出了一种报警评估机制,有效提高方案的检测准确度,减少了预测误差或信道误码所带来的误报。仿真实验结果表明,Markov模型具有较高的预测精度,能够实时地预测传感器网络流量;MPDD方案能够快速、有效地检测拒绝服务攻击且消耗资源较少。     

Detecting stealth DHCP starvation attack using machine learning approach

Dynamic Host Configuration Protocol (DHCP) is used to automatically configure clients with IP address and other network configuration parameters. Due to absence of any in-built authentication, the protocol is vulnerable to a class of Denial-of-Service (DoS) attacks, popularly known as DHCP starvation attacks. However, known DHCP starvation attacks are either ineffective in wireless networks or not stealthy in some of the network topologies. In this paper, we first propose a stealth DHCP starvation attack which is effective in both wired and wireless networks and can not be detected by known detection mechanisms. We test the effectiveness of proposed attack in both IPv4 and IPv6 networks and show that it can successfully prevent other clients from obtaining IP address, thereby, causing DoS scenario. In order to detect the proposed attack, we also propose a Machine Learning (ML) based anomaly detection framework. In particular, we use some popular one-class classifiers for the detection purpose. We capture IPv4 and IPv6 traffic from a real network with thousands of devices and evaluate the detection capability of different machine learning algorithms. Our experiments show that the machine learning algorithms can detect the attack with high accuracy in both IPv4 and IPv6 networks.     

K-DDoS-SDN: A distributed DDoS attacks detection approach for protecting SDN environment

Software-defined networking (SDN) is an advanced networking paradigm that decouples forwarding control logic from the data plane. Therefore, it provides a loosely-coupled architecture between the control and data plane. This separation provides flexibility in the SDN environment for addressing any transformations. Further, it delivers a centralized way of managing networks due to control logic embedded in the SDN controller. However, this advanced networking paradigm has been facing several security issues, such as topology spoofing, exhausting bandwidth, flow table updating, and distributed denial of service (DDoS) attacks. A DDoS attack is one of the most powerful menaces to the SDN environment. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we propose a Kafka-based distributed DDoS attacks detection approach for protecting the SDN environment named K-DDoS-SDN. The K-DDoS-SDN consists of two modules: (i) Network traffic classification (NTClassification) module and (ii) Network traffic storage (NTStorage) module. The NTClassification module is the detection approach designed using scalable H2O ML techniques in a distributed manner and deployed an efficient model on the two-nodes Kafka Streams cluster to classify incoming network traces in real-time. The NTStorage module collects raw packets, network flows, and 21 essential attributes and then systematically stores them in the HDFS to re-train existing models. The proposed K-DDoS-SDN designed and evaluated using the recent and publically available CICDDoS2019 dataset. The average classification accuracy of the proposed distributed K-DDoS-SDN for classifying network traces into legitimate and one of the most popular attacks, such as DDoS_UDP is 99.22%. Further, the outcomes demonstrate that proposed distributed K-DDoS-SDN classifies traffic traces into five categories with at least 81% classification accuracy.     

SDN中基于C4.5决策树的DDoS攻击检测

SDN（Software Defined Network,软件定义网络）是一种新兴的网络架构,它的控制与转发分离架构为网络管理带来了极大的便利性和灵活性,但同时也带来新的安全威胁和挑战。攻击者通过对SDN的集中式控制器进行DDoS（Distributed Denial of Service,分布式拒绝服务）攻击,会使信息不可达,造成网络瘫痪。为了检测DDoS攻击,提出了一种基于C4.5决策树的检测方法：通过提取交换机流表项信息,使用C4.5决策树算法训练数据集生成决策树对流量进行分类,实现DDoS攻击的检测,最后通过实验证明了该方法有更高的检测成功率,更低的误警率与较少的检测时间。     

Early Detection of DDoS Attacks Against Software Defined Network Controllers

Software Defined Network (SDN) is a new network architecture that has an operating system. Unlike conventional production networks, SDN allows more flexibility in network management using that operating system that is called the controller. The main advantage of having a controller in the network is the separation of the forwarding and the control planes, which provides central control over the network. Although central control is the major advantage of SDN, it is also a single point of failure if it is made unreachable by a Distributed Denial of Service (DDoS) attack. In this paper, that single point of failure is addressed by utilizing the controller to detect such attacks and protect the SDN architecture of the network in its early stages. The two main objectives of this paper are to (1) make use of the controller’s broad view of the network to detect DDoS attacks and (2) propose a solution that is effective and lightweight in terms of the resources that it uses. To accomplish these objectives, this paper examines the effect of DDoS attacks on the SDN controller and the way it can exhaust controller resources. The proposed solution to detect such attacks is based on the entropy variation of the destination IP address. Based on our experimental setup, the proposed method can detect DDoS within the first 250 packets of the attack traffic.     

内容中心网络中DoS攻击问题综述

“内容中心网络”（Content Centric Networking,CCN）是未来互联网架构体系群中极具前景的架构之一。尽管CCN网络的全新设计使其能够抵御目前网络存在的大多数形式DoS攻击,但仍引发了新型的DoS攻击,其中危害较大的两类攻击是兴趣包泛洪攻击和缓存污染攻击。这两类DoS攻击利用了CCN网络自身转发机制的安全逻辑漏洞,通过泛洪大量的恶意攻击包,耗尽网络资源,并导致网络瘫痪。与传统IP网络中DoS攻击相比,CCN网络中的内容路由、内嵌缓存和接收者驱动传输等新特征,对其DoS攻击的检测和防御方法都提出了新的挑战。本文首先介绍CCN网络的安全设计和如何对抗已有的DoS攻击,然后从多角度描述、比较CCN中新型DoS攻击的特点,重点阐述了兴趣包泛洪攻击和缓存污染攻击的分类、检测和防御方法,以及它们所面临的问题挑战,最后对全文进行总结。     

Ant-based IP traceback

The denial-of-service (DoS) attacks with the source IP address spoofing techniques has become a major threat to the Internet. An intrusion detection system is often used to detect DoS attacks and to coordinate with the firewall to block them. However, DoS attack packets consume and may exhaust all the resources, causing degrading network performance or, even worse, network breakdown. A proactive approach to DoS attacks is allocating the original attack host(s) issuing the attacks and stopping the malicious traffic, instead of wasting resources on the attack traffic.

In this paper, an ant-based traceback approach is proposed to identify the DoS attack origin. Instead of creating a new type or function or processing a high volume of fine-grained data used by previous research, the proposed traceback approach uses flow level information to identify the origin of a DoS attack.

Two characteristics of ant algorithm, quick convergence and heuristic, are adopted in the proposed approach on finding the DoS attack path. Quick convergence efficiently finds out the origin of a DoS attack; heuristic gives the solution even though partial flow information is provided by the network.

The proposed method is evaluated through simulation on various network environments and two simulated real networks, NSFNET and DFN. The simulation results show that the proposed method can successfully and efficiently find the DoS attack path in various simulated network environments, with full and partial flow information provided by the networks.     

动态主机配置协议泛洪攻击在软件定义网络中的实时防御

在软件定义网络（SDN）中，动态主机配置协议（DHCP）泛洪攻击报文通常能通过reactive方式主动地进入控制器，对SDN危害巨大。针对传统的DHCP泛洪攻击防御方法无法阻止SDN中该攻击带来的控制链路阻塞这一问题，提出一种DHCP泛洪攻击的动态防御机制（DDM）。DDM包含检测模型和缓解模型。在检测模型中，不同于他人提出的静态阈值检测方法，采用DHCP流量均速和IP池余量两个关键参数建立动态峰值估计模型来评估端口是否受到攻击，若受到攻击则交由缓解模型进行防御。在缓解模型中，利用地址解析协议（ARP）的应答特点进行IP池清洗，并设计了周期内分时段拦截机制对攻击源进行截流，在缓解阻塞的同时，最大限度减少拦截对用户正常使用的影响。仿真实验结果表明，相对静态阈值检测，DDM检测误差平均降低18.75%。DDM缓解模型能高效地拦截流量，同时将用户在拦截期正常接入网络的等待时间平均缩短81.45%。     

基于OpenDayLight的恶意扫描防护技术

针对分布式拒绝服务（DDoS）攻击难以在危害产生之前被检测和防御的问题,提出了一种基于软件定义网络（SDN）的面向恶意扫描的控制层实时防护机制。首先,分析了SDN相比传统网络在网络层防护技术上的优势;其次,针对网络攻击手段——恶意扫描,提出了面向恶意扫描的控制层实时防护机制,该机制在SDN集中控制式架构的基础上,充分利用OpenDayLight （ODL）控制器所提供的表述性状态传递（REST）应用程序编程接口（API）开发外部应用,实现了对底层交换机端口的检测、判定、防护三个环节;最后,对给出的方案在ODL平台上进行了编程实现,并实验测试了恶意扫描的检测防御方案。实验结果表明：当有端口正在对网络进行恶意扫描时,面向恶意扫描的控制层实时防护机制可以及时禁用该端口,实时起到对恶意扫描攻击的防护作用,进而在分布式拒绝服务攻击当中具有破坏性的行为还未开始时就对其进行了预防。     

抵御SIP分布式洪泛攻击的入侵防御系统

针对SIP分布式洪泛攻击检测与防御的研究现状,结合基于IP的分布式洪泛攻击和SIP消息的特点,提出了一种面向SIP分布式洪泛攻击的两级防御分布式拒绝服务(DDoS)攻击体系结构(TDASDFA):一级防御子系统(FDS)和二级防御子系统(SDS)。FDS对SIP的信令流进行粗粒度检测与防御,旨在过滤非VoIP消息和丢弃超出指定速率的IP地址的SIP信令,保证服务的可用性;SDS利用一种基于安全级别设定的攻击减弱方法对SIP信令流进行细粒度检测,并过滤具有明显DoS攻击特征的恶意攻击和低流量攻击。FDS和SDS协同工作来实时检测网络状况,减弱SIP分布式洪泛攻击。实验结果表明,TDASDFA能实时地识别和防御SIP分布式洪泛攻击,并且在异常发生时有效地减弱SIP代理服务器/IMS服务器被攻击的可能性。     

基于包络延拓和本征波匹配的时变DoS攻击频谱检测

DoS攻击信号具有非平稳时变特性,湮没在色噪声背景的复杂网络环境中,对之难以有效检测.传统方法中采用基于非平稳时变信号处理的Hough变换单谱脉冲响应检测算法,由于二次型时频分布的边缘效应会引起较大包络衰减,检测性能不好.因此提出一种基于包络延拓和本征波匹配的时变DoS攻击信号频谱检测算法来对DoS攻击检测信号进行双曲调频分解,构建信号数学演化模型,得到信号包络和本征波特征提取结果.采用双线性Hough变换法分析频谱特征畸变,进行瞬时频率估计,得到信号的单谱脉冲响幅频响应,在包络时频特征空间优化搜索路径实现包络延拓,基于最小均方误差准则设计本征波匹配滤波器,控制DoS频谱偏移,实现信号频谱检测.仿真结果表明,本算法能在强色噪声背景干扰下提高检测性能,检测概率高于传统算法,且能准确估计参量信息,提高对DoS攻击信号的主动防御能力.     

基于主成分分析的拒绝服务和网络探测攻击检测

针对拒绝服务和网络探测攻击难以检测的问题,提出了一种新的基于主成分分析的拒绝服务和网络探测攻击检测方法。首先在攻击流量和正常流量数据集上应用主成分分析,得到所有流量数据集的各种不同统计量;然后依据这些统计量构造攻击检测模型。实验表明：该模型检测拒绝服务和网络探测攻击的检测率达到99%;同时能够让受攻击对象在有限的时间内做出反应,减少攻击对服务器的危害程度。