一种新的DDoS攻击检测算法

为了准确及时的进行DDoS攻击检测,提出了一种新的DDoS攻击检测算法。该算法在基于传统的小波分析检测DDoS攻击的基础上融入了主成分分析法和小波分析法中DDoS检测方法,并根据该算法设计相应的模型和算法来检测 DDoS 攻击,并且引入信息论中的信息熵对源IP地址的分散程度进行度量,根据初始阶段Hurst指数及熵值的变化自适应地设定阈值以检测攻击的发生。实验结果表明,该方法大幅度的提高了DDoS检测的速度。     

基于IP地址分段的DDoS路由追踪技术研究

DDoS攻击已经成为当今网络安全的最大隐患。文中提出了基于IP地址分段的DDoS路由追踪技术，分析了边界路由器IP地址重叠分段方法以及在地址重构过程中误报的产生，阐述了降低误报率的方法。最后．通过在模拟环境下，验证了该路由追踪技术误报率低、所需边界路由器少、地址重建复杂度小。     

基于聚集算法的DDoS数据流检测和处理

提出了一种应用于路由器的嵌入式DDoS（分布式拒绝服务攻击）防御算法.针对DDoS攻击的本质特征,对IP数据流进行轻量级协议分析,把IP数据流分为TCP、UDP和ICMP（网间控制报文协议）数据流,分别建立相应的聚集模式,根据该模式来检测DDoS聚集所占资源,采取相应的抑制措施过滤攻击数据包,从而保证合法数据流的正常转发.仿真试验证明该方法能准确地检测到DDoS攻击,处理效果很好.     

奥运版权保护技术方案

一 基于IP地址信息库的区域保护方案 1．概述 利用IP地址信息资源对掌控网站的访客来源非常有效。我们所拥有的全球IP地址信息库收集了多方资源、信息准确并持续更新。利用该IP地址信息库．可以轻松得知网站来访用户的地理位置信息．可以精确到如国家、城市、所处地区、网络运营商等信息。     

路由器防范拒绝服务攻击技术研究

拒绝服务攻击给网络安全带来了巨大的威胁，防范DDoS攻击一直是安全领域的一个重要课题。介绍了路由器防范拒绝服务攻击的技术，包括IP路径重构技术、在源端防范DDoS策略、防范IP地址欺骗的机制和基于拥塞控制的方法，指出了进一步的研究方向。     

分布式拒绝服务攻击追踪方法的分析

分布式拒绝服务攻击（DDoS）给政府部门和商业机构的网络安全造成严重威胁。IP追踪技术能够反向追踪IP数据包到它们的源头,所以是识别和阻止DDoS攻击的重要一步。本文针对DDoS攻击,对比分析了各个IP反向追踪方法的基本原理和优缺点。     

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。     

分布式DNS反射DDoS攻击检测及控制技术

分布式DNS反射DDoS攻击已经成为拒绝服务攻击的主要形式之一,传统的基于网络流量统计分析和网络流量控制技术已经不能满足防护需求。提出了基于生存时间值（TTL）智能研判的DNS反射攻击检测技术,能够准确发现伪造源IP地址分组;基于多系统融合的伪造源地址溯源阻断技术,从源头上阻断攻击流量流入网络。     

防范DDoS攻击

到目前为止,针对分布式拒绝服务攻击（DDoS）的防御依然没有特别直接有效的方法,因为这种攻击会利用TCP／IP协议的漏洞。除非您完全不使用TCP／IP,才有可能抵御DDoS攻击。     

基于NTP反射放大攻击的DDoS追踪研究

提出了一种利用NTP反射型放大攻击的特点,通过对中国大陆开放公共NTP服务的主机定期发起主动探测（执行monlist指令）,利用返回信息对全球范围NTP反射类DRDoS攻击事件进行长期追踪观察和统计分析。追踪从2014年2月开始,初始探测范围为大陆近1.4万台NTP服务主机,每隔2 h一个周期持续进行了164天,观测到了针对数十万个IP地址的疑似DDoS攻击行为。     

Efficient DDoS attack detection and prevention scheme based on SDN in cloud environment

For addressing the problem of two typical types of distributed denial of service (DDoS) attacks in cloud environment,a DDoS attack detection and prevention scheme called SDCC based on software defined network (SDN) architecture was proposed.SDCC used a combination of bandwidth detection and data flow detection,utilized confidence-based filtering (CBF) method to calculate the CBF score of packets,judged the packet of CBF score below the threshold as an attacking packet,added its attribute information to the attack flow feature library,and sent the flow table to intercept it through SDN controller.Simulation results show that SDCC can detect and prevent different types of DDoS attacks effectively,and it has high detection efficiency,reduces the controller’s computation overhead,and achieves a low false positive rate.     

A novel IPv6 traceback architecture using COPS protocol

In any Distributed Denial of Service (DDoS) attack, invaders may use incorrect or spoofed Internet Protocol (IP) addresses in the attacking packets and thus disguise the actual origin of the attacks. This is primarily due to the stateless nature of the Internet. IP traceback algorithms provide mechanisms for identifying the true source of an IP datagram on the Internet ensuring at least the accountability of cyber attacks. While many IP traceback techniques have been proposed, most of the previous studies focus and offer solutions for DDoS attacks done on Internet Protocol version 4 (IPv4) environment. IPv4 and IPv6 networks differ greatly from each other, which urge the need of traceback techniques specifically tailored for IPv6 networks. In this paper, we propose a novel traceback architecture for IPv6 networks using Common Open-Policy Service and a novel packet-marking scheme. We also provide complete underlying protocol details required for traceback support in IPv6 networks. The proposed architecture is on demand and only single packet is required to traceback the attack.     

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.     

基于深度学习的实时DDoS攻击检测

分布式拒绝服务(DDoS)攻击是一种分布式、协作式的大规模网络攻击方式,提出了一种基于深度学习的DDoS攻击检测方法,该方法包含特征处理和模型检测两个阶段:特征处理阶段对输入的数据分组进行特征提取、格式转换和维度重构;模型检测阶段将处理后的特征输入深度学习网络模型进行检测,判断输入的数据分组是否为DDoS攻击分组.通过ISCX2012数据集训练模型,并通过实时的DDoS攻击对模型进行验证.结果表明,基于深度学习的DDoS攻击检测方法具有高检测精度、对软硬件设备依赖小、深度学习网络模型易于更新等优点.     

Defense Against Spoofed IP Traffic Using Hop-Count Filtering

IP spoofing has often been exploited by Distributed Denial of Service (DDoS) attacks to: 1)conceal flooding sources and dilute localities in flooding traffic, and 2)coax legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victim servers is essential to their own protection and prevention of becoming involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he cannot falsify the number of hops an IP packet takes to reach its destination. More importantly, since the hop-count values are diverse, an attacker cannot randomly spoof IP addresses while maintaining consistent hop-counts. On the other hand, an Internet server can easily infer the hop-count information from the Time-to-Live (TTL) field of the IP header. Using a mapping between IP addresses and their hop-counts, the server can distinguish spoofed IP packets from legitimate ones. Based on this observation, we present a novel filtering technique, called Hop-Count Filtering (HCF)-which builds an accurate IP-to-hop-count (IP2HC) mapping table-to detect and discard spoofed IP packets. HCF is easy to deploy, as it does not require any support from the underlying network. Through analysis using network measurement data, we show that HCF can identify close to 90% of spoofed IP packets, and then discard them with little collateral damage. We implement and evaluate HCF in the Linux kernel, demonstrating its effectiveness with experimental measurements     

Topology-assisted deterministic packet marking for IP traceback

A novel deterministic packet marking (DPM) for IP traceback against denial of service (DoS) and distributed denial of service (DDoS) attacks is presented, which features good scalability and high accuracy. In this scheme, an ingress router pre-calculates a Hash of its IP address and splits the Hash into several fragments. When marking a packet, the router randomly selects a fragment to mark into the packet. In the traceback stage the victim identifies the marked router with the help of the map of its upstream routers. Based on the map, the victim can identify a candidate ingress router after receiving only several marked packets. The scheme overcomes defects in previous deterministic packet marking schemes, where too much packets are required to recover a router and high false positive rate occurs in case of large-scale DDoS. Theoretical analysis, the pseudo code and experimental results are provided. The scheme is proved to be accurate and efficient and can handle large-scale DDoS attacks.     

StackPi: New Packet Marking and Filtering Mechanisms for DDoS and IP Spoofing Defense

Today's Internet hosts are threatened by large-scale distributed denial-of-service (DDoS) attacks. The path identification (Pi) DDoS defense scheme has recently been proposed as a deterministic packet marking scheme that allows a DDoS victim to filter out attack packets on a per packet basis with high accuracy after only a few attack packets are received (Yaar , 2003). In this paper, we propose the StackPi marking, a new packet marking scheme based on Pi, and new filtering mechanisms. The StackPi marking scheme consists of two new marking methods that substantially improve Pi's incremental deployment performance: Stack-based marking and write-ahead marking. Our scheme almost completely eliminates the effect of a few legacy routers on a path, and performs 2–4 times better than the original Pi scheme in a sparse deployment of Pi-enabled routers. For the filtering mechanism, we derive an optimal threshold strategy for filtering with the Pi marking. We also develop a new filter, the PiIP filter, which can be used to detect Internet protocol (IP) spoofing attacks with just a single attack packet. Finally, we discuss in detail StackPi's compatibility with IP fragmentation, applicability in an IPv6 environment, and several other important issues relating to potential deployment of StackPi.