IPv6环境下SYN-Flood攻击防范的研究

在分析几种常规防范DDoS方法特点和不足的基础上,结合数据包流量检测技术和改进的TCP Cookie技术,在IPv6环境下建立了一种数据包检测--智能过滤PDIF(Packet Detection and Intelligence Filter)防御SYN Flood 攻击的模型.通过检测数据包流量是否超出闻值来分析是否发生攻击,同时采用一种验证远程客户端TCP连接有效性的算法来实现智能过滤,将DDoS攻击分组拦截而让正常的网络流量通过,在实验室测试中取得了较好的效果.     

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。     

基于SYN Cookie的DDoS防御技术研究

文章讨论了防御SYN Flood类型DDoS攻击的基本原理和技术,通过对比几种SYN Cookie方案的技术细节,提出了一种改进方案和相应的系统模型。     

非对称路由环境下SYN flood攻击防御方法

针对现有网络安全设施无法有效防御非对称路由环境下流量规模较大的SYN flood攻击的问题,对SYN flood攻击检测技术和TCP连接管理策略进行研究,提出了一种轻量级攻击检测和混合连接管理策略相结合的防御方法,利用SYN分组比例和目的地址熵进行攻击检测,并根据检测结果对基于SYN的连接管理策略和基于数据的连接管理策略进行灵活切换。实验证明该防御方法能有效地减轻SYN flood攻击对网络安全设施的影响。     

使用补偿非参数CUSUM方法检测DDoS攻击

提出了一种有效的、在攻击目的端检测DDoS攻击的方法.研究目的是为了在花费较少代价的情况下,早期检测到攻击的存在,记录可疑的攻击包.对基于TCP协议的DDoS攻击而言,从受害端进行观察,会发现很多没有确认的TCP报文段.在每个时间间隔 ,计算未确认的报文段个数与总报文段个数的比值,形成基于时间的统计序列,再用经过改进的非参数递归CUSUM(cumulative sum)算法在线、快速检测DDoS攻击,并在检测的同时记录可疑的攻击包.经实验验证,该检测算法不仅快速,而且具有更低的误报率,能够适应更复杂的网络检测环境.另外,还能为攻击的分析取证和追踪提供一定帮助.     

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.     

TCP协议的安全研究

由于TCP的内在脆弱性及在Internet网中的重要作用,导致其易受到电脑黑客的种种攻击。该文重点分析了常用的SYN泛滥、TCP序列号攻击、TCP会话劫持和RST／FIN攻击等手段,并给出了相应的解决方法。     

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。     

TCP/IP协议的网络安全

论文介绍了TCP/IP协议的总体概况、结构功能;TCP/IP协议的安全隐患;然后从SYN的攻击代码来分析TCP/IP协议,并且实现了防御SYN的方法.     

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。     

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.     

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.     

SYN‐Guard: An effective counter for SYN flooding attack in software‐defined networking

In software‐defined networking (SDN), TCP SYN flooding attack is considered as one of the most effective attacks to perform control plane and target server saturation. In this attack, an attacker generates a large number of malicious SYN requests, and because of the absence of the forwarding rules, the data plane switches have to forward these SYN messages to the controller. This excessive forwarding causes congestion over the communication channel between a data plane and control plane, and it also exhausts computational resources at both the planes. In this paper, we propose a novel countermeasure called SYN‐Guard to detect and prevent SYN flooding in SDN networks. We fully implement SYN‐Guard on the SDN controller to validate the incoming TCP connection requests. The controller installs forwarding rules for the SYN requests that successfully clear the validation test of SYN‐Guard. The host of the fake SYN request is detected, and SYN‐Guard prevents it from sending any further SYN requests to the data plane switch. The performance evaluation done using the simulation results shows that SYN‐Guard exhibits low side effect for genuine TCP requests, and when compared with standard SDN and state‐of‐art proposals, it reduces the average response time up to 21% during an ongoing SYN flooding attack.     

A Large-Scale Hidden Semi-Markov Model for Anomaly Detection on User Browsing Behaviors

Many methods designed to create defenses against distributed denial of service (DDoS) attacks are focused on the IP and TCP layers instead of the high layer. They are not suitable for handling the new type of attack which is based on the application layer. In this paper, we introduce a new scheme to achieve early attack detection and filtering for the application-layer-based DDoS attack. An extended hidden semi-Markov model is proposed to describe the browsing behaviors of web surfers. In order to reduce the computational amount introduced by the model's large state space, a novel forward algorithm is derived for the online implementation of the model based on the M-algorithm. Entropy of the user's HTTP request sequence fitting to the model is used as a criterion to measure the user's normality. Finally, experiments are conducted to validate our model and algorithm.      

Differentiating Malicious DDoS Attack Traffic from Normal TCP Flows by Proactive Tests

To defend against distributed denial of service (DDoS) attacks, one critical issue is to effectively isolate the attack traffic from the normal ones. A novel DDoS defense scheme based on TCP is hereby contrived because TCP is the dominant traffic for both the normal and lethal flows in the Internet. Unlike most of the previous DDoS defense schemes that are passive in nature, the proposal uses proactive tests to identify and isolate the malicious traffic. Simulation results validate the effectiveness of our proposed scheme     

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.     

An intelligent method for real-time detection of DDoS attack based on fuzzy logic

The paper puts forward a variance-time plots method based on slide-window mechanism to calculate the Hurst parameter to detect Distribute Denial of Service（DDoS）attack in real time.Based on fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoS judgment mechanism is designed.This new method calculates the Hurst parameter quickly and detects DDoS attack in real time.Through comparing the detecting technologies based on statistics and feature-packet respectively under different experiments,it is found that the new method can identify the change of the Hurst parameter resulting from DDoS attack traffic with different intensities,and intelligently judge DDoS attack self-adaptively in real time.     

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。