新网络环境下应用层DDoS攻击的剖析与防御

针对新网络环境下近两年新出现的应用层分布式拒绝服务攻击,本文将详细剖析其原理与特点,并分析现有检测机制在处理这种攻击上的不足.最后,本文提出一种基于用户行为的检测机制,它利用Web挖掘的方法通过Web访问行为与正常用户浏览行为的偏离程度检测与过滤恶意的攻击请求,并通过应用层与传输层的协作实现对攻击源的隔离.     

Denial of service detection using dynamic time warping

With the rapid growth of security threats in computer networks, the need for developing efficient security-warning systems is substantially increasing. Distributed denial-of-service (DDoS) and DoS attacks are still among the most effective and dreadful attacks that require robust detection. In this work, we propose a new method to detect TCP DoS/DDoS attacks. Since analyzing network traffic is a promising approach, our proposed method utilizes network traffic by decomposing the TCP traffic into control and data planes and exploiting the dynamic time warping (DTW) algorithm for aligning these two planes with respect to the minimum Euclidean distance. By demonstrating that the distance between the control and data planes is considerably small for benign traffic, we exploit this characteristic for detecting attacks as outliers. An adaptive thresholding scheme is implemented by adjusting the value of the threshold in accordance with the local statistics of the median absolute deviation (MAD) of the distances between the two planes. We demonstrate the efficacy of the proposed method for detecting DoS/DDoS attacks by analyzing traffic data obtained from publicly available datasets.     

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.     

ALPi: A DDoS Defense System for High-Speed Networks

Distributed denial-of-service (DDoS) attacks pose a significant threat to the Internet. Most solutions proposed to-date face scalability problems as the size and speed of the network increase, with no widespread DDoS solution deployed in the industry. PacketScore has been proposed as a proactive DDoS defense scheme, which detects DDoS attacks, differentiates attack packets from legitimate ones with the use of packet scoring (where the score of a packet is calculated based on attribute values it possesses), and discards packets whose scores are lower than a dynamic threshold. In this paper, we propose ALPi, a new scheme which extends the packet scoring concept with reduced implementation complexity and enhanced performance. More specifically, a leaky-bucket overflow control scheme simplifies the score computation, and facilitates high-speed implementation. An attribute-value-variation scoring scheme analyzes the deviations of the current traffic attribute values, and increases the accuracy of detecting and differentiating attacks. An enhanced control-theoretic packet discarding method allows both schemes to be more adaptive to challenging attacks such as those with ever-changing signatures and intensities. When combined together, the proposed extensions not only greatly reduce the memory requirement and implementation complexity but also substantially improve the accuracies in attack detection and packet differentiation. This makes ALPi an attractive DDoS defense system amenable for high-speed hardware implementation.     

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.     

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。     

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。     

大规模拒绝服务（DDoS）攻击的防御与清洗系统

首先建立DDoS攻击特征的选择、表示、分析以及模型求解。然后,在此基础上研究基于敏感访问参数可变阈值约束的DDoS攻击防御方法。最后,研究基于可变概率标记的DDoS攻击流量清洗技术。     

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.     

Efficient and low‐cost defense against distributed denial‐of‐service attacks in SDN‐based networks

Distributed are common threats in many networks, where attackers attempt to make victim servers unavailable to other users by flooding them with worthless requests. These attacks cannot be easily stopped by firewalls, since they forge lots of connections to victims with various IP addresses. The paper aims to exploit the software‐defined networking (SDN) technique to defend against DDoS attacks. However, the controller has to handle lots of connections launched by DDoS attacks, which burdens it with a heavy load and degrades SDN's performance. Therefore, the paper proposes an efficient and low‐cost DDoS defense (ELD) mechanism for SDN. It adopts a nested reverse‐exponential data storage scheme to help the controller efficiently record the information of packets in the limited memory. Once there are many packets with high IP variability sent to a certain server and this situation lasts for a while, then a DDoS attack is likely happening. In this case, the controller asks switches to block malicious connections by installing flow rules. Experimental results verify that the ELD mechanism rapidly recognizes protocol‐based DDoS attacks and stops them in time, including TCP SYN flood, UDP flood, and ICMP flood, and also greatly reduces the overhead for the controller to defend against attacks. Moreover, ELD can distinguish DDoS flows from legitimate ones with similar features such as elephant flows and impulse flows, thereby eliminating false alarms.     

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。     

一种新型拒绝服务攻击的研究

Shrew DDoS(Distributed Denial of Service)攻击是一种新型的DDoS攻击,也称低速率DDoS攻击。它是利用TCP超时重传机制的漏洞,通过估计合法TCP流的RTO(Retransmission timeout)作为低速率攻击发包的周期T,周期性的发送短脉冲,使得攻击流可以周期性地占用网络带宽,这样就会让合法的TCP流总是认为网络的负担很重,造成所有受其影响的TCP流进入超时重传状态,最终使得受害主机的吞吐量大幅度降低,从而达到攻击目的。由于其攻击速率低,可以躲避传统的高速率攻击防御机制。这种新型拒绝服务攻击具有隐蔽性好、效果明显的特点。     

一种新的DDoS攻击主动防御方案

介绍DDoS攻击原理和分析DDoS攻击网络的控制机制后,提出了一种新的基于蜜网(honeynet)的主动防御方案,利用网络陷阱与跟踪技术,从根源上阻止DDoS攻击远程控制网络的形成,以达到主动防御的目的。     

Cross-Layer Enhancement to Support TCP-Based Traffics in WLANs

Widespread deployment of wireless local area networks and a gradual increase in streaming applications have brought about a demand for improved quality of service (QoS) in wireless networks. However, increasing user datagram protocol based high priority multimedia traffic and the class differentiation introduced in QoS protocols, has resulted into transmission control protocol (TCP) starvation and increased spurious timeouts. While today’s Internet traffic is still dominated by TCP based applications, the negative effects of IEEE 802.11e enhanced distributed coordination function (EDCF) scheme on TCP performance in the presence of high priority traffic have not been extensively explored. In this paper, the performance of TCP in 802.11e WLAN competing with high priority traffic is examined. The prioritised adaptive enhanced scheme (PAD_EDCF) is proposed. The proposed scheme gives priority to TCP control packets in order to improve the low traffic transmission flow and acquires additional capability of adjusting the MAC parameters based on the traffic load condition. Simulation results demonstrate that the proposed scheme significantly improves TCP performances in terms of traffic efficiency, throughput and reduces delay.     

DDoS攻击从检测到流量识别总体防御方案研究

分布式拒绝服务（DDoS）攻击是互联网安全的严重威胁,攻击发生时会有大规模流量淹没目标网络和主机。能够准确快速地检测到攻击,区分合法拥塞流量和攻击流量,对攻击流量加以清洗,对于DDoS攻击的防御来说十分重要。采用信息熵对流量参数进行实时统计来检测攻击,用累积和（CUSUM）算法控制熵值连续变化情况。检测到攻击后,依据目的IP数量前后增长情况找出受害者,对流向受害者处的流量进行重点观察。由于大规模的攻击流量与合法的拥塞流量非常相似,难以识别,在此对流本身的相似性进行考察,使用流相关系数算法辨别攻击流量和合法拥塞流量,为流量清洗工作提供依据。     

Monitoring the Application-Layer DDoS Attacks for Popular Websites

Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when such attacks mimic or occur during the flash crowd event of a popular Website. Focusing on the detection for such new DDoS attacks, a scheme based on document popularity is introduced. An Access Matrix is defined to capture the spatial-temporal patterns of a normal flash crowd. Principal component analysis and independent component analysis are applied to abstract the multidimensional Access Matrix. A novel anomaly detector based on hidden semi-Markov model is proposed to describe the dynamics of Access Matrix and to detect the attacks. The entropy of document popularity fitting to the model is used to detect the potential application-layer DDoS attacks. Numerical results based on real Web traffic data are presented to demonstrate the effectiveness of the proposed method.      

基于自适应阀值的SYN Flooding攻击防御

针对危害性极大的SYN Flooding攻击,提出了一种新的基于自适应阀值的防御系统.该系统监控出/入终端网络TCP业务的平衡性,实时自适应调整攻击检测阀值和限速门限,提高了检测的准确性和在线检测速度,有效地滤除攻击流,同时向合法业务提供良好的服务.     

SEDP‐based detection of low‐rate DoS attacks

Low‐rate Denial of Service (LDoS) is a new type of TCP‐targeted attacks, which attempt to deny bandwidth to TCP flows while sending at sufficiently low‐average rate to elude detection of DoS defense system. Therefore, LDoS attacks are difficult to be detected by routers and counter‐DoS mechanisms. In this paper, an approach of detecting LDoS attacks is proposed by using the technology of signal processing based on the model of spectral energy distribution probability. The proposed approach calculates variances between the incoming traffic of normal TCP and attack flows to a server by using packet sampling sequence within a certain period. The network traffic is converted from the time domain to the frequency domain forming a spectral signal, and the distribution probability of spectral energy is estimated based on spectrum characteristics of rectangular pulses. This approach explores that the energy of LDoS attacks is mostly distributed in the main lobe width while that of normal TCP traffic is just concentrated near zero in frequency domain. Both the spectral energy of normal TCP traffic and LDoS attacks distributed in main lobe are calculated, and an energy threshold is set as decision value based on statistical results according to energy distribution properties. The existence of LDoS attacks is determined and detected by comparing calculated variances with the preset decision threshold value. Tests on the detection performance of the proposed approach were performed in NS‐2 simulation environment, and detection rate was obtained by Hypothesis test. Experiment results show that the proposed approach has higher detection accuracy and less computation consuming. Copyright © 2014 John Wiley & Sons, Ltd.     

TCP-Aware Uplink Scheduling for IEEE 802.16

In IEEE 802.16 networks, a bandwidth request-grant mechanism is used to accommodate various QoS requirements of heterogeneous traffic. However, it may not be effective for TCP flows since (a) there is no strict QoS requirement in TCP traffic; and (b) it is difficult to estimate the amount of required bandwidth due to dynamic changes of the sending rate. In this letter, we propose a new uplink scheduling scheme for best-effort TCP traffic in IEEE 802.16 networks. The proposed scheme does not need any bandwidth request process for allocation. Instead, it estimates the amount of bandwidth required for a flow based on its current sending rate. Through simulation, we show that the proposed scheme is effective to allocate bandwidth for TCP flows     

基于DDoS的TCP SYN攻击与防范

分布式拒绝服务攻击(DDoS)是出现在这几年的一种具有很强攻击力而又缺乏有效防御手段的Internet攻击手段,是目前网络安全界研究的热点.TCP SYN洪流攻击是最常见的DDoS攻击手段之一.文中在对DDoS攻击进行深入研究的基础上,着重对TCP SYN洪流攻击及其防范措施进行了深入研究,提出了一种新的综合攻击检测技术,较好地解决了对此类攻击的防范问题.