Optimization of Stealthwatch Network Security System for the Detection and Mitigation of Distributed Denial of Service (DDoS) Attack: Application to Smart Grid System

The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.

基于多模态神经网络流量特征的网络应用层DDoS攻击检测方法

农业设备、传感器和监控系统与网络的连接日益紧密,给农村配电网带来了新的网络安全挑战。其中,分布式拒绝服务(DDoS)攻击是一种常见的网络威胁,对农村配电网的安全性构成了严重威胁。针对农村配电网的特殊需求,提出一种基于多模态神经网络流量特征的网络应用层DDoS攻击检测方法。通过制定网络应用层流量数据包捕获流程并构建多模态神经网络模型,成功提取并分析了网络应用层DDoS攻击流量的特征。在加载DDoS攻击背景下的异常流量特征后,计算相关系数并设计相应的DDoS攻击检测规则,以实现对DDoS攻击的有效检测。经试验分析,所提出的方法在提取DDoS攻击相关特征上表现出色,最大提取完整度可达95%,效果明显优于对比试验中基于EEMD-LSTM的检测方法和基于条件熵与决策树的检测方法。

面向边缘计算的TCA1C DDoS检测模型

边缘计算弥补了传统云计算数据传输开销大的不足,但边缘网络中存储和计算资源受限的特殊性限制了其部署复杂安全算法的能力,更易受到分布式拒绝服务(DDoS)攻击。针对目前边缘网络中DDoS攻击检测方法性能不高、未对卸载任务分类处理、对多属性的流量处理能力弱的问题,提出一种基于任务分类的Attention-1D-CNN DDoS检测模型TCA1C,对通信链路中的流量按不同的卸载任务进行分类,使单个任务受到攻击时不会影响整个链路中计算任务卸载的安全性,再对同一任务下的流量提取属性值并进行归一化处理。处理后的数据输入到Attention-1D-CNN,通道Attention和空间Attention学习数据特征对DDoS检测的贡献度,利用筛选函数剔除低于特征阈值的冗余信息,降低模型学习过程的复杂度,使模型快速收敛。仿真结果表明:TCA1C模型在缩短DDoS检测所用时间的情况下,检测准确率高达99.73%,检测性能优于DT、ELM、LSTM和CNN;当多个卸载任务在面临特定攻击概率时,卸载任务分类能有效降低不同任务的相互影响,使终端设备的计算任务在卸载过程中保持较高的安全性。

Formalized Description of Distributed Denial of Service Attack

The distributed denial of service (DDoS) attack is one of the dangers in intrusion modes. It's difficult to defense and can cause serious damage to the system. Based on a careful study of the attack principles and characteristics, an object-oriented formalized description is presented, which contains a three-level framework and offers full specifications of all kinds of DDoS modes and their features and the relations between one another. Its greatest merit lies in that it contributes to analyzing, checking and judging DDoS. Now this formalized description has been used in a special IDS and it works very effectively.(

Entropy-Based Approach to Detect DDoS Attacks on Software Defined Networking Controller

The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues.However,despite the advantages of centralized control,concern about its security is rising.The more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A Distributed Denial of Service(DDoS)attack on the controller could cripple the entire network.For that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive rate.This paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or targets.The proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying rates.Using two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.

Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.

McAfee SecurityCenter Evaluation under DDoS Attack Traffic

During the Distributed Denial of Service (DDoS) attacks, computers are made to attack other computers. Newer Firewalls now days are providing prevention against such attack traffics. McAfee SecurityCenter Firewall is one of the most popular security software installed on millions of Internet connected computers worldwide. “McAfee claims that if you have installed McAfee SecurityCentre with anti-virus and antispyware and Firewall then you always have the most current security to combat the ever-evolving threats on the Internet for the duration of the subscription”. In this paper, we present our findings regarding the effectiveness of McAfee SecurityCentre software against some of the popular Distributed Denial Of Service (DDoS) attacks, namely ARP Flood, Ping-flood, ICMP Land, TCP-SYN Flood and UDP Flood attacks on the computer which has McAfee SecurityCentre installed. The McAfee SecurityCentre software has an in built firewall which can be activated to control and filter the Inbound/Outbound traffic. It can also block the Ping Requests in order to stop or subside the Ping based DDoS Attacks. To test the McAfee Security Centre software, we created the corresponding attack traffic in a controlled lab environment. It was found that the McAfee Firewall software itself was incurring DoS (Denial of Service) by completely exhausting the available memory resources of the host computer during its operation to stop the external DDoS Attacks.

The History, Trend, Types, and Mitigation of Distributed Denial of Service Attacks

Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global information source for every being. Despite all this, attacker knowledge by cybercriminals has advanced and resulted in different attack methodologies on the internet and its data stores. This paper will discuss the origin and significance of Denial of Service (DoS) and Distributed Denial of Service (DDoS). These kinds of attacks remain the most effective methods used by the bad guys to cause substantial damage in terms of operational, reputational, and financial damage to organizations globally. These kinds of attacks have hindered network performance and availability. The victim’s network is flooded with massive illegal traffic hence, denying genuine traffic from passing through for authorized users. The paper will explore detection mechanisms, and mitigation techniques for this network threat.

DDoS攻击检测综述

结合DDoS攻击检测方法的最新研究情况,对DDoS攻击检测技术进行系统分析和研究,对不同检测方法进行比较,讨论了当前该领域存在的问题及今后研究的方向。

基于IP熵变量的DDoS攻击溯源模型

针对当前溯源方法无法识别源于中间路由器的恶意攻击、无法区分攻击流量类型等问题,借鉴热力学中熵的概念并结合IP分布特征,定义IP熵变量,结合通信熵和IP熵提出一个基于熵变量的DDoS攻击溯源模型,设计DDoS攻击识别算法、DDoS攻击溯源算法和DDoS流量区分算法。实验结果表明,该模型在时间容忍范围内提升了溯源效率,降低了僵尸网络检测的漏报率,能够识别出快速DDoS攻击、慢速DDoS攻击及flash crowd等类型,识别率达到了85.71%。

基于Web行为轨迹的应用层DDoS攻击防御模型

为了有效防御应用层分布式拒绝服务攻击(DDoS),定义了一种搭建在Web应用服务器上的基于Web行为轨迹的防御模型。把用户的访问行为抽象为Web行为轨迹,根据攻击请求的生成方式与用户访问Web页面的行为特征,定义了四种异常因素,分别为访问依赖异常、行为速率异常、轨迹重复异常、轨迹偏离异常。采用行为轨迹化简算法简化行为轨迹的计算,然后计算用户正常访问网站时和攻击访问时产生的异常因素的偏离值,来检测针对Web网站的分布式拒绝服务攻击,在检测出某用户产生攻击请求时,防御模型禁止该用户访问来防御DDoS。实验采用真实数据当作训练集,在模拟不同种类攻击请求下,防御模型短时间识别出攻击并且采取防御机制抵制。实验结果表明,Web行为轨迹的防御模型能够有效防御针对Web网站的分布式拒绝服务攻击。

基于源目的IP地址对数据库的防范DDos攻击策略

提出了一种基于源目的IP地址对数据库的防范分布式拒绝服务攻击(distributed denial of service attacks,简称DDos)攻击策略.该策略建立正常流量的源目的IP地址对数据库(source and destination IP address database,简称SDIAD),使用扩展的三维BloomFilter表存储SDIAD,并采用改进的滑动窗口无参数CUSUM(cumulative sum)算法对新的源目的IP地址对进行累积分析,以快速准确地检测出DDos攻击.对于SDIAD的更新,采用延迟更新策略,以确保SDIAD的及时性、准确性和鲁棒性.实验表明,该防范DDos攻击策略主要应用于边缘路由器,无论是靠近攻击源端还是靠近受害者端,都能够有效地检测出DDos攻击,并且有很好的检测准确率.

基于TCP缓存的DDoS攻击检测算法

由拒绝服务攻击(DoS)发展而来的分布式拒绝服务攻击(DDoS)已成为目前网络安全的主要威胁之一。从分析TCP缓存入手,提出一种基于缓冲区检测的DDoS检测算法。结合历史连接记录来对TCP缓存进行分析,生成特征向量,通过BP神经网络检测TCP缓存异常程度,根据异常程度判断是否发生攻击。实验结果表明,该算法能迅速准确地检测出DDoS攻击,有效阻止DDoS攻击的发生。

云环境下Web服务应用层DDoS攻击检测系统

针对云计算环境中的Web服务应用层容易遭受攻击的问题,提出一种用来检测XML和HTTP层分布式拒绝服务(DDo S)攻击的防御系统,并嵌入到云环境中,实现对云中介和云服务提供商的保护。从属于特定简单对象访问协议(SOAP)正常操作中提取数据集的特征值,构建相应的高斯请求模型;对Web服务的网络服务描述语言(WSDL)中的一些属性进行设置,实现对攻击的初步过滤;对服务请求的HTTP头部和XML内容进行检查,并与模型数据比较,进一步实现攻击检测。实验结果表明,该系统能够有效地预防多种DDo S攻击,且消耗较少的响应时间。

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。

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。

基于软件定义网络的DDoS攻击检测方法及其应用

根据分布式拒绝服务(DDoS)攻击特性与OpenFlow技术,提出一种基于软件定义网络(SDN)的DDoS攻击检测方法。获取OpenFlow交换机中安装的流表项,构建针对目的地址的流表特征值,并采用支持向量机对训练样本进行分类,实现DDoS攻击的检测。通过将该DDoS攻击检测方法进行原型系统实现并集成到SDN网络环境中,验证了该方法的正确性和有效性,并表明其能在提高DDoS攻击行为检测率的同时明显降低误报率,具有较好的综合检测性能。

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。

基于流量分析的App-DDoS攻击检测

针对当前应用层分布式拒绝服务攻击(App-DDoS)检测方法高度依赖于系统日志,且检测攻击类型单一的问题,提出了基于卡尔曼滤波和信息熵的联合检测模型DFM-FA(detection and filtering model against App-DDoSattacks based on flow analysis),将应用层的行为异常检测映射为网络层的流量异常检测,最大限度地保证了合法用户的优先正常访问。实验证明,DFM-FA既不依赖于系统日志,同时又能检测到FTP、DNS等多种App-DDoS攻击。

基于自相似流量检测的DDoS攻击及防御研究

DDoS攻击是目前互联网中应用最广泛,也是危害性最大的一种攻击方式,以破坏网络服务的可用性为目标。文章首先介绍了DDoS的攻击原理,并重点对DDoS的攻击方式和基于自相似网络流量的DDoS攻击检测方式进行研究,最后针对目前DDoS攻击的防御方式进行了全面解析。