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.

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.(

Denial of Service Due to Direct and Indirect ARP Storm Attacks in LAN Environment

ARP-based Distributed Denial of Service (DDoS) attacks due to ARP-storms can happen in local area networks where many computer systems are infected by worms such as Code Red or by DDoS agents. In ARP attack, the DDoS agents constantly send a barrage of ARP requests to the gateway, or to a victim computer within the same sub-network, and tie up the resource of attacked gateway or host. In this paper, we set to measure the impact of ARP-attack on resource exhaustion of computers in a local area network. Based on attack experiments, we measure the exhaustion of processing and memory resources of a victim computer and also other computers, which are located on the same network as the victim computer. Interestingly enough, it is observed that an ARP-attack not only exhausts resource of the victim computer but also significantly exhausts processing resource of other non-victim computers, which happen to be located on the same local area network as the victim computer.

Central Aggregator Intrusion Detection System for Denial of Service Attacks

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles(EVs)to be used by the smart grid through the central aggregator.Since the central aggregator is connected to the smart grid through a wireless network,it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system.However,existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network.In this paper,the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed.The proposed system,central aggregator–intrusion detection system(CA-IDS),works as a security gateway for EVs to analyze andmonitor incoming traffic for possible DoS attacks.EVs are registered with a Central Aggregator(CAG)to exchange authenticated messages,and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG.A denial of service(DoS)attack is simulated at CAG in a vehicle-to-grid(V2G)network manipulating various network parameters such as transmission overhead,receiving capacity of destination,average packet size,and channel availability.The proposed system is compared with existing intrusion detection systems using different parameters such as throughput,jitter,and accuracy.The analysis shows that the proposed system has a higher throughput,lower jitter,and higher accuracy as compared to the existing schemes.

Experimental Evaluation of Cisco ASA-5510 Intrusion Prevention System against Denial of Service Attacks

Cyber attacks are continuing to hamper working of Internet services despite increase in the use of network security systems such as, firewalls and Intrusion protection systems (IPS). Recent Denial of Service (DoS) attack on Independence Day weekend, on July 4th, 2009 launched to debilitate the US and South Korean governments’ websites is indicative of the fact that the security systems may not have been adequately deployed to counteract such attacks. IPS is a vital security device which is commonly used as a front line defense mechanism to defend against such DoS attacks. Before deploying a firewall or an IPS device for network protection, in many deployments, the performance of firewalls is seldom evaluated for their effectiveness. Many times, these IPS’s can become bottleneck to the network performance and they may not be effective in stopping DoS attacks. In this paper, we intend to drive the point that deploying IPS may not always be effective in stopping harmful effects of DoS attacks. It is important to evaluate the capability of IPS before they are deployed to protect a network or a server against DoS attacks. In this paper, we evaluate performance of a commercial grade IPS Cisco ASA-5510 IPS to measure its effectiveness in stopping a DoS attacks namely TCP-SYN, UDP Flood, Ping Flood and ICMP Land Attacks. This IPS comes with features to counteract and provide security against these attacks. Performance of the IPS is measured under these attacks protection and compared with its performance when these protection features were not available (i.e. disabled). It was found that the IPS was unable to provide satisfactory protection despite the availability of the protection features against these flooding attacks. It is important for the network managers to measure the actual capabilities of an IPS system before its deployment to protect critical information infrastructure.

Modeling and Simulation of Low Rate of Denial of Service Attacks

The low-rate denial of service attack is more applicable to the network in recent years as a means of attack, which is different from the traditional field type DoS attacks at the network end system or network using adaptive mechanisms exist loopholes flow through the low-rate periodic attacks on the implementation of high-efficiency attacked by an intruder and not be found, resulting in loss of user data or a computer deadlock. LDos attack since there has been extensive attention of researchers, the attack signature analysis and detection methods to prevent network security have become an important research topic. Some have been proposed for the current attacks were classified LDoS describe and model, and then in NS-2 platform for experimental verification, and then LDoS attack detection to prevent difficulties are discussed and summarized for the future such attacks detection method research work to provide a reference.

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.

基于CNN-BiLSTM的ICMPv6 DDoS攻击检测方法

针对ICMPv6网络中DDoS攻击检测问题,提出一种基于CNN-BiLSTM网络的检测算法。通过将带有注意力机制、DropConnect和Dropout混合使用加入到CNN-BiLSTM算法中,防止在训练过程中产生的过拟合问题,同时更准确地提取数据的特性数据。通过实验表明:提出的算法在多次实验中的检测准确率、误报率与漏报率平均值分别为92.84%、4.49%和10.54%,检测算法泛化性较强,性能由于其他算法,能够有效处理ICMPv6 DDoS攻击检测问题。

DoS攻击下基于水声传感器网络的目标追踪算法

考虑水下拒绝服务(DoS)攻击和声线分层效应的影响,研究了基于水声传感器网络(UASN)的目标追踪问题。首先考虑由水下传感器、水面浮标和水下目标组成的UASN架构。基于此,构造了水下目标运动模型和水下Dos攻击模型,提出了一种改进的一致性无迹卡尔曼滤波(UKF)水下目标追踪算法。进一步,证明了追踪算法的收敛性,推导了算法的克拉美罗下界。仿真和实验结果表明,文中算法可以在水下环境有效进行目标追踪,基于一致性UKF的算法提高了跟踪精度。

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。

DOS攻击下饱和脉冲多智能体系统的安全定制化一致性

提出并解决一种饱和脉冲多智能体系统在拒绝服务(Denial of service,DOS)攻击环境中的安全定制化一致性控制问题.首先引入微分机制和加权策略,构建一种带可调参数一致性模式项的系统模型,以满足复杂场景对一致性的定制化需求.其次结合饱和效应和脉冲机制,为系统设计一种满足执行器功率受限约束的饱和脉冲控制协议.再次采用切换拓扑分析DOS攻击下系统的网络拓扑结构,并采用李雅普洛夫稳定性和矩阵测度理论,得到系统实现安全定制化一致性的充分条件.最后通过仿真实验和对比分析,验证了所提理论的有效性和优越性.

融合稀疏注意力机制在DDoS攻击检测中的应用

针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型GVBNet(global variable block net),使用攻击密度自适应计算稀疏注意力。利用信息熵以及信息增益分析提取攻击流量的连续字节作为特征向量,通过构建基于GVBNet的网络模型在两种数据集上进行训练。实验结果表明,该方法具有良好的识别效果、检测速度以及抗干扰能力,在不同的环境下具有应用价值。

SDN中DDoS攻击检测研究综述

软件定义网络(SDN)的出现弥补了传统网络的不足并为网络管理带来技术革新。分布式拒绝服务(DDoS)攻击作为网络安全领域的主要威胁之一,严重影响着SDN这一新兴网络架构。随着SDN技术的部署及发展,如何在SDN中检测DDoS攻击成为当前研究领域的热点与难点。为了对相关研究工作进行合理综述,根据所使用的核心技术或理论的不同,将DDoS攻击检测方法划分为基于信息熵、基于机器学习、基于深度学习三大类。介绍SDN体系架构并分析SDN中的DDoS攻击,同时介绍一些常用的公开数据集和评估指标,从四个角度归纳和分析近年来相关研究人员提出的模型或算法,总结了SDN中的DDoS攻击检测研究领域的未来研究方向并进行展望,为该领域的相关研究人员提供研究思路。

DDoS Attack Detection Scheme Based on Entropy and PSO-BP Neural Network in SDN

SDN (Software Defined Network) has many security problems, and DDoS attack is undoubtedly the most serious harm to SDN architecture network. How to accurately and effectively detect DDoS attacks has always been a difficult point and focus of SDN security research. Based on the characteristics of SDN, a DDoS attack detection method combining generalized entropy and PSOBP neural network is proposed. The traffic is pre-detected by the generalized entropy method deployed on the switch, and the detection result is divided into normal and abnormal. Locate the switch that issued the abnormal alarm. The controller uses the PSO-BP neural network to detect whether a DDoS attack occurs by further extracting the flow features of the abnormal switch. Experiments show that compared with other methods, the detection accurate rate is guaranteed while the CPU load of the controller is reduced, and the detection capability is better.

Stochastic DoS Attack Allocation Against Collaborative Estimation in Sensor Networks

In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is studied.In the communication channels between sensors and a remote estimator,the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped.A stochastic power allocation approach composed of three steps is proposed.Firstly,the minimum number of channels and the channel set to be attacked are given.Secondly,a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems,respectively.Finally,by converting the original coupling nonlinear programming problem to a linear programming problem,a method of searching attack probabilities and power to minimize the attack energy is proposed.The effectiveness of the proposed scheme is verified by simulation examples.

The detection method of low-rate DoS attack based on multi-feature fusion

As a new type of Denial of Service(DoS)attacks,the Low-rate Denial of Service(LDoS)attacks make the traditional method of detecting Distributed Denial of Service Attack(DDoS)attacks useless due to the characteristics of a low average rate and concealment.With features extracted from the network traffic,a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper.An attack feature set containing the Acknowledge character(ACK)sequence number,the packet size,and the queue length is used to classify normal and LDoS attack traffics.Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor(KNN)classifier separately,and to obtain the decision contour matrix.Then a posteriori probability in the matrix is fused,and the fusion decision index D is used as the basis of detecting the LDoS attacks.Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.

DDoS攻击的发展与检测技术研究

在网络安全领域,分布式拒绝服务攻击(DDoS)因其对信息系统的严重威胁而备受关注。研究了DDoS攻击的演进历程,从20世纪90年代的洪水攻击到使用僵尸网络、物联网(IoT)设备和云服务的复杂攻击模式,展示了攻击方法和技术的多样化。还对DDoS攻击进行了详细的分类,包括基于攻击手段和流量的分类方法,以及基于流量分析、行为分析、机器学习和深度学习的检测技术,指出了这些技术的优点和局限性。此外,还介绍了当下较为成熟的DDoS检测工具与系统,如Suricata Snort,Bro(Zeek)以及防护服务Cloudflare,并讨论了DDoS攻击未来的发展趋势。

SDN中基于信息熵与机器学习的DDoS攻击检测模型构建

软件定义网络(Software-Defined Network,SDN)的集中控制特征使得网络管理更加灵活高效,但同时也成为网络攻击的主要对象,其中分布式拒绝服务攻击DDoS是SDN面临的主要威胁之一。结合统计学习和机器学习这2种SDN中常用的检测方法,文章分析了基于信息熵与机器学习算法的DDoS攻击检测模型,并利用信息熵的阈值判断检测出疑似异常流量,再用决策树算法构建的检测模型检测出DDoS攻击。分类检测模型构建了6个特征属性,并通过计算信息增益值筛选出最优特征子集。通过与其他分类算法模型的比较,该模型提高了检测准确性,减少了检测时间。

A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

The recent development of cloud computing offers various services on demand for organization and individual users,such as storage,shared computing space,networking,etc.Although Cloud Computing provides various advantages for users,it remains vulnerable to many types of attacks that attract cyber criminals.Distributed Denial of Service(DDoS)is the most common type of attack on cloud computing.Consequently,Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks.Since DDoS attacks have become increasingly widespread,it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks.Further,the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent.In this research work,DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory(LSTM)model have been proposed with NSL-KDD dataset.In Hybrid LSTM method,the Particle Swarm Optimization(PSO)technique,which is combined to optimize the weights of the LSTM neural network,reduces the prediction error.This deep belief network method is used to extract the features of IP packets,and it identifies DDoS attacks based on PSO-LSTM model.Moreover,it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks.The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine(SVM)and LSTM in terms of attack detection performance along with the results of the measurement of accuracy,recall,f-measure,precision.

基于深度森林的多类型DDoS攻击检测方法

分布式拒绝服务攻击(DDoS)是网络安全的主要威胁之一。近年来,基于多种不同DDoS攻击方式的混合攻击数量大幅增长,如何在保证精度的前提下同时检测多种类型的DDoS攻击成为亟待解决的问题。为此,提出一种基于深度森林的多类型DDoS攻击检测方法。该方法首先使用基于平均不纯度的特征选择算法对多类型异常流量数据集进行特征排序与特征筛选;然后使用多粒度扫描对DDoS训练集进行特征提取,并使用级联森林分层训练模型,最终生成可用于DDoS恶意流量检测与分类的深度森林模型。实验结果表明,与6种主流树类集成学习模型相比,基于改进深度森林的DDoS攻击检测方法训练得到的分类器准确率最低提升了0.8%,召回率最低提升了0.9%;与改进前相比,改进后模型准确率提升了1.3%,加权召回率提高了1.3%,训练时间减少了29.7%。模型整体性能有明显提升。