Network traffic fusion and analysis against DDoS flooding attacks with a novel reversible sketch

Distributed Denial of Service (DDoS) flooding attacks are one of the typical attacks over the Internet. They aim to prevent normal users from accessing specific network resources. How to detect DDoS flooding attacks arises a significant and timely research topic. However, with the continuous increase of network scale, the continuous growth of network traffic brings great challenges to the detection of DDoS flooding attacks. Incomplete network traffic collection or non-real-time processing of big-volume network traffic will seriously affect the accuracy and efficiency of attack detection. Recently, sketch data structures are widely applied in high-speed networks to compress and fuse network traffic. But sketches suffer from a reversibility problem that it is difficult to reconstruct a set of keys that exhibit abnormal behavior due to the irreversibility of hash functions. In order to address the above challenges, in this paper, we first design a novel Chinese Remainder Theorem based Reversible Sketch (CRT-RS). CRT-RS is not only capable of compressing and fusing big-volume network traffic but also has the ability of reversely discovering the anomalous keys (e.g., the sources of malicious or unwanted traffic). Then, based on traffic records generated by CRT-RS, we propose a Modified Multi-chart Cumulative Sum (MM-CUSUM) algorithm that supports self-adaptive and protocol independent detection to detect DDoS flooding attacks. The performance of the proposed detection method is experimentally examined by two open source datasets. The experimental results show that the method can detect DDoS flooding attacks with efficiency, accuracy, adaptability, and protocol independability. Moreover, by comparing with other attack detection methods using sketch techniques, our method has quantifiable lower computation complexity when recovering the anomalous source addresses, which is the most important merit of the developed method.     

基于混合深度学习的多类型低速率DDoS攻击检测方法

低速率分布式拒绝服务攻击针对网络协议自适应机制中的漏洞实施攻击,对网络服务质量造成了巨大威胁,具有隐蔽性强、攻击速率低和周期性的特点.现有检测方法存在检测类型单一和识别精度低的问题,因此提出了一种基于混合深度学习的多类型低速率DDoS攻击检测方法.模拟不同类型的低速率DDoS攻击和5G环境下不同场景的正常流量,在网络入...     

一种基于数据挖掘的DDoS攻击入侵检测系统

防御分布式拒绝服务(DDoS)攻击是当前网络安全中最难解决的问题之一。针对该问题文章设计了基于数据挖掘技术的入侵检测系统，使用聚类k-means方法结合Apriori关联规则，较好地解决了数值属性的分类问题，从数据中提取流量特征产生检测模型。实验表明，该系统可以有效检测DDoS攻击。     

基于地址相关度的分布式拒绝服务攻击检测方法

分布式拒绝服务(DDoS)攻击检测是网络安全领域的研究热点.对DDoS攻击的研究进展及其特点进行了详细分析,针对DDoS攻击流的流量突发性、流非对称性、源IP地址分布性和目标IP地址集中性等本质特征提出了网络流的地址相关度(ACV)的概念.为了充分利用ACV,提高方法的检测质量,提出了基于ACV的DDoS攻击检测方法,通过自回归模型的参数拟合将ACV时间序列变换为多维空间内的AR模型参数向量序列来描述网络流状态特征,采用支持向量机分类器对当前网络流状态进行分类以识别DDoS攻击.实验结果表明,该检测方法能够有效地检测DDoS攻击,降低误报率.     

基于集成学习的多类型应用层DDoS攻击检测方法

针对应用层分布式拒绝服务（DDoS）攻击类型多、难以同时检测的问题,提出了一种基于集成学习的应用层DDoS攻击检测方法,用于检测多类型的应用层DDoS攻击。首先,数据集生成模块模拟正常和攻击流量,筛选并提取对应的特征信息,并生成表征挑战黑洞（CC）、HTTP Flood、HTTP Post及HTTP Get攻击的47维特征信息;其次,离线训练模块将处理后的有效特征信息输入集成后的Stacking检测模型进行训练,从而得到可检测多类型应用层DDoS攻击的检测模型;最后,在线检测模块通过在线部署检测模型来判断待检测流量的具体流量类型。实验结果显示,与Bagging、Adaboost和XGBoost构建的分类模型相比,Stacking集成模型在准确率方面分别提高了0.18个百分点、0.21个百分点和0.19个百分点,且在最优时间窗口下的恶意流量检测率达到了98%。验证了所提方法对多类型应用层DDoS攻击检测的有效性。     

Robust and efficient detection of DDoS attacks for large-scale internet

In recent years, distributed denial of service (DDoS) attacks have become a major security threat to Internet services. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. In this paper, we propose a novel framework to robustly and efficiently detect DDoS attacks and identify attack packets. The key idea of our framework is to exploit spatial and temporal correlation of DDoS attack traffic. In this framework, we design a perimeter-based anti-DDoS system, in which traffic is analyzed only at the edge routers of an internet service provider (ISP) network. Our framework is able to detect any source-address-spoofed DDoS attack, no matter whether it is a low-volume attack or a high-volume attack. The novelties of our framework are (1) temporal-correlation based feature extraction and (2) spatial-correlation based detection. With these techniques, our scheme can accurately detect DDoS attacks and identify attack packets without modifying existing IP forwarding mechanisms at routers. Our simulation results show that the proposed framework can detect DDoS attacks even if the volume of attack traffic on each link is extremely small. Especially, for the same false alarm probability, our scheme has a detection probability of 0.97, while the existing scheme has a detection probability of 0.17, which demonstrates the superior performance of our scheme.     

基于流量行为特征的DoS&DDoS攻击检测与异常流识别

针对现有方法仅分析粗粒度的网络流量特征参数,无法在保证检测实时性的前提下识别出拒绝服务(DoS)和分布式拒绝服务(DDoS)的攻击流这一问题,提出一种骨干网络DoS&DDoS攻击检测与异常流识别方法。首先,通过粗粒度的流量行为特征参数确定流量异常行为发生的时间点;然后,在每个流量异常行为发生的时间点对细粒度的流量行为特征参数进行分析,以找出异常行为对应的目的IP地址;最后,提取出与异常行为相关的流量进行综合分析,以判断异常行为是否为DoS攻击或者DDoS攻击。仿真实验的结果表明,基于流量行为特征的DoS&DDoS攻击检测与异常流识别方法能有效检测出骨干网络中的DoS攻击和DDoS攻击,并且在保证检测实时性的同时,准确地识别出与攻击相关的网络流量     

基于宏观网络流相关性的DDoS攻击检测

针对现行分布式拒绝服务(DDoS)攻击检测方法存在检测效率低、适用范围小等缺陷,在分析DDoS攻击对网络流量大小和IP地址相关性影响的基础上,提出基于网络流相关性的DDoS攻击检测方法。对流量大小特性进行相关性分析,定义Hurst指数方差变化率为测度,用以区分正常流量与引起流量显著变化的异常性流量。研究IP地址相关性,定义并计算IP地址相似度作为突发业务流和DDoS攻击的区分测度。实验结果表明,对网络流中流量大小和IP地址2个属性进行相关性分析,能准确地区分出网络中存在的正常流量、突发业务流和DDoS攻击,达到提高DDoS攻击检测效率的目的。     

SDN场景中基于双向流量特征的DDoS攻击检测方法

传统网络资源的分布式特性使得管理员较难实现网络的集中管控,在分布式拒绝服务攻击发生时难以快速准确地检出攻击并溯源。针对这一问题,结合软件定义网络集中管控、动态管理的优势和分布式拒绝服务攻击特点,本文首先引入双向流量概念,提出了攻击检测四元组特征,并利用增长型分层自组织映射算法对网络流中提取的四元组特征向量快速准确地分析并分类,同时提出了一种通过自适应改变监控流表粒度以定位潜在受害者的检测方法。仿真实验结果表明,本文提出的四元组特征及下发适量监控流表项的检测算法能以近似96%的准确率检出攻击并定位受害者,且对控制器造成的计算开销较小。     

Semi-supervised machine learning approach for DDoS detection

Even though advanced Machine Learning (ML) techniques have been adopted for DDoS detection, the attack remains a major threat of the Internet. Most of the existing ML-based DDoS detection approaches are under two categories: supervised and unsupervised. Supervised ML approaches for DDoS detection rely on availability of labeled network traffic datasets. Whereas, unsupervised ML approaches detect attacks by analyzing the incoming network traffic. Both approaches are challenged by large amount of network traffic data, low detection accuracy and high false positive rates. In this paper we present an online sequential semi-supervised ML approach for DDoS detection based on network Entropy estimation, Co-clustering, Information Gain Ratio and Exra-Trees algorithm. The unsupervised part of the approach allows to reduce the irrelevant normal traffic data for DDoS detection which allows to reduce false positive rates and increase accuracy. Whereas, the supervised part allows to reduce the false positive rates of the unsupervised part and to accurately classify the DDoS traffic. Various experiments were performed to evaluate the proposed approach using three public datasets namely NSL-KDD, UNB ISCX 12 and UNSW-NB15. An accuracy of 98.23%, 99.88% and 93.71% is achieved for respectively NSL-KDD, UNB ISCX 12 and UNSW-NB15 datasets, with respectively the false positive rates 0.33%, 0.35% and 0.46%.     

基于时间序列分析的应用层DDoS攻击检测

根据正常用户和攻击者在访问行为上的差异,提出一种基于IP请求熵(SRE)时间序列分析的应用层分布式拒绝服务(DDoS)攻击检测方法。该方法通过拟合SRE时间序列的自适应自回归(AAR)模型,获得描述当前用户访问行为特征的多维参数向量,并使用支持向量机(SVM)对参数向量进行分类来识别攻击。仿真实验表明,该方法能够准确区分正常流量和DDoS攻击流量,适用于大流量背景下攻击流量没有引起整个网络流量显著变化的DDoS攻击的检测。     

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。     

基于网络全局流量异常特征的DDoS攻击检测

由于分布式拒绝服务（DDoS）攻击的隐蔽性和分布式特征，提出了一种基于全局网络的DDoS检测方法。与传统检测方法只对单条链路或者受害者网络进行检测的方式不同，该方法对营运商网络中的OD流进行检测。该方法首先求得网络的流量矩阵，利用多条链路中攻击流的相关特性，使用K L变换将流量矩阵分解为正常和异常流量空间，分析异常空间流量的相关特征，从而检测出攻击。仿真结果表明该方法对DDoS攻击的检测更准确、更快速，有利于DDoS攻击的早期检测与防御。     

一种基于小波分析的DDoS攻击检测方法

通过对网络流量的分形特性和分布式拒绝服务（DDoS）的特点进行研究,提出了一种基于小波分析的DDoS攻击检测方法,并设计了该方法检测攻击的模型。对网络流量的分形特性进行判断,然后对具有自相似特性和多重分形特性的网络流量,分别采用基于小波分析的Hurst指数方差法和基于多窗口小波分析的Holder指数法检测DDoS攻击。通过对DARPA 2000年数据的实验表明,该方法能够有效地检测到攻击,对大流量背景攻击、低速率攻击、反射式攻击也都达到了较高的检测率,比传统方法有效。     

应用层DDOS攻击检测技术研究

随着检测底层DDoS攻击的技术不断成熟和完善,应用层DDoS攻击越来越多。由于应用层协议的复杂性,应用层DDoS攻击更具隐蔽性和破坏性,检测难度更大。通过研究正常用户访问的网络流量特征和应用层DDoS攻击的流量特征,采用固定时间窗口内的请求时间间隔以及页面作为特征。通过正常用户和僵尸程序访问表现出不同的特点,对会话进行聚类分析,从而检测出攻击,经过实验,表明本检测算法具有较好的检测性能。     

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。     

基于智能蜂群算法的DDoS攻击检测系统

随着大数据应用的普及,DDoS攻击日益严重并已成为主要的网络安全问题。针对大数据环境下的DDoS攻击检测问题,设计了一种融合聚类和智能蜂群算法(DFSABC_elite)的DDoS攻击检测系统。该系统将聚类算法与智能蜂群算法相结合来进行数据流分类,用流量特征分布熵与广义似然比较判别因子来检测DDoS攻击数据流的特征,从而实现了DDoS攻击数据流的高效检测。实验结果显示,该系统在类内紧密度、类间分离度、聚类准确率、算法耗时和DDoS检测准确率方面明显优于基于并行化K-means的普通蜂群算法和基于并行化K-means算法的DDoS检测方法。     

Deciding optimal entropic thresholds to calibrate the detection mechanism for variable rate DDoS attacks in ISP domain: honeypot based approach

High bandwidth DDoS attacks consume more resources and have direct impact at ISP level in contrast to low rate DDoS attacks which lead to graceful degradation of network and are mostly undetectable. Although an array of detection schemes have been proposed, current requirement is a real time DDoS detection mechanism that adapts itself to varying network conditions to give minimum false alarms. DDoS attacks that disturb the distribution of traffic features in ISP domain are reflected by entropic variations on in stream samples. We propose honeypot detection for attack traffic having statistically similar distribution features as legitimate traffic. Next we propose to calibrate the detection mechanism for minimum false alarm rate by varying tolerance factor in real time. Simulations are carried out in ns-2 at different attack strengths. We also report our experimental results over MIT Lincoln lab dataset and its subset KDD 99 dataset. Results show that the proposed approach is comparable to previously reported approaches with an advantage of variable rate attack detection with minimum false positives and negatives.     

基于用户浏览行为的HTTP Flood检测方法

与传统的基于低层协议的DDoS攻击相比,应用层DDoS具有更加显著的攻击效果,而且更加难以检测。现有的解决方法包括：特征检测、流量限制、隐半马尔可夫模型等。这些方法在检测应用层DDoS攻击（如,HTTP Get Flood）攻击时检测率不高或者检测速度较慢。提出的基于用户浏览行为的检测方法对HTTPFlood攻击检测效果明显得到改善。     

神经网络和IP标记在DDoS攻击防御中的应用

DDoS(Distributed Denial of Service)攻击是在传统的DoS攻击上产生的新的网络攻击方式,是Internet面临的最严峻威胁之一,这种攻击带来巨大的网络资源消耗,影响正常的网络访问.DDoS具有分布式特征,攻击源隐蔽,而且该类攻击采用IP伪造技术,不易追踪和辨别.任何网络攻击都会产生异常流量,DDoS也不例外,分布式攻击导致这种现象更加明显.主要研究利用神经网络技术并借助IP标记辅助来甄别异常流量中的网络数据包,方法是:基于DDoS攻击总是通过多源头发起对单一目标攻击的特点,通过IP标记技术对路由器上网路包进行标记,获得反映网络流量的标记参数,作为神经网络的输入参数相量;再对BP神经网络进行训练,使其能识别DDoS攻击引起的异常流量;最后,训练成熟的神经网络即可在运行时有效地甄别并防御DDoS攻击,提高网络资源的使用效率.通过实验证明了神经网络技术防御DDoS攻击是可行和高效的.