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.

Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

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.(

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

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.

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.

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。

基于合约熵判决算法的区块链网络DDoS防御优化

为针对多域协同联合防御分布式拒绝服务(DDoS)更有效发挥区块链网络优势,该文提出智能合约熵检测(SCED)算法。基于Hyperledger Fabric区块链架构,首先,通过智能合约技术构建多域协作机制,建立智能合约协作子算法;然后,针对受害域内非法流量IP生成IP黑名单,并通知所有协作域,协同防御DDoS;其次,在各单域内部署由监测、比对、分类及防御模块组成的熵判决防御子算法,检测处理域内非法流量;最后,结合多域智能合约协作和单域熵判决防御,实现区块链网络中受害域、中间域及攻击域协同防御DDoS。仿真结果表明,对比ChainSecure等算法,SCED算法在精度和效率方面有较好的表现。

面向网络靶场的DDoS攻击缓解方法研究

本文提出一种面向不平衡数据的DDoS攻击检测模型,提升对DDoS洪泛攻击的检测效果。以OpenStack为核心技术设计网络靶场,并使用Ceph分布式存储替换OpenStack原生存储系统,提出一种OpenStack与Ceph的超融合网络靶场方案,可以实现对计算、存储、网络资源的统一管理。首先,针对Ceph集群在存储时的数据分布不均情况对平台存储性能的影响,提出一种基于好感度的数据存储优化算法,利用好感度因子约束数据的存储位置,有效提高集群中所有OSD节点存储数据的均衡性。同时,设计了一种基于软件定义网络(Software Defined Network,SDN)的DDoS洪泛攻击检测与缓解方法,有效降低了对物理设备性能的要求,最后结合Ryu控制器的可编程性,实现DDoS洪泛攻击缓解方法。

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 tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

DDoS Detection for 6G Internet of Things: Spatial-Temporal Trust Model and New Architecture

With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the access network of 6G IoT.Based on historical communication behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.

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。

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.

AN APPROACH OF DEFENDING AGAINST DDOS ATTACK

An approach of defending against Distributed Denial of Service (DDoS) attack based on flow model and flow detection is presented. The proposed approach can protect targets from DDoS attacking, and allow targets to provide good service to legitimate traffic under DDoS attacking, with fast reaction. This approach adopts the technique of dynamic comb filter, yields a low level of false positives of less than 1.5%, drops similar percentage of good traffic, about 1%, and passes neglectable percentage of attack bandwidth to the victim, less than 1.5%. The prototype of commercial product, D-fighter, is developed by implementing this proposed approach on Intel network processor platform IXP1200.

Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

The emergence of a new network architecture,known as Software Defined Networking(SDN),in the last two decades has overcome some drawbacks of traditional networks in terms of performance,scalability,reliability,security,and network management.However,the SDN is vulnerable to security threats that target its controller,such as low-rate Distributed Denial of Service(DDoS)attacks,The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component.Therefore,there is an urgent need to propose a detection approach for this type of attack with a high detection rate and low false-positive rates.Thus,this paper proposes an approach to detect low-rate DDoS attacks on the SDN controller by adapting a dynamic threshold.The proposed approach has been evaluated using four simulation scenarios covering a combination of low-rate DDoS attacks against the SDN controller involving(i)a single host attack targeting a single victim;(ii)a single host attack targeting multiple victims;(iii)multiple hosts attack targeting a single victim;and(iv)multiple hosts attack targeting multiple victims.The proposed approach’s average detection rates are 96.65%,91.83%,96.17%,and 95.33%for the above scenarios,respectively;and its average false-positive rates are 3.33%,8.17%,3.83%,and 4.67%for similar scenarios,respectively.The comparison between the proposed approach and two existing approaches showed that it outperformed them in both categories.

SDN中基于统计与集成自编码器的DDoS攻击检测模型

软件定义网络(Software-defined Networking,SDN)是一种提供细颗粒集中网络管理服务的新型网络体系结构,主要有控制与转发分离、集中控制和开放接口基本特征。SDN由于控制层的集中管理逻辑,控制器被攻击者作为理想的分布式拒绝服务攻击(Distributed Denial-of-Service,DDoS)目标。然而,传统的基于统计的DDoS攻击检测算法常存在误报率高、阈值固定等问题;基于机器学习模型的检测算法常存在计算资源消耗大、泛化性差等问题。为此,文中提出了一种基于统计特征与集成自编码器的DDoS攻击双层检测模型。基于统计的方法提取Rényi熵特征,设置动态阈值判断可疑流量;基于集成自编码器算法对可疑流量进行更精确的DDoS攻击判断。双层检测模型不仅提升了检测效果,解决了误报率高的问题,同时还有效地缩短了检测时间,从而减少了计算资源的消耗。实验结果表明,该模型在不同网络环境下都有较高的准确率,不同数据集检测的F1值最低都达到了98.5%以上,表现出了很强的泛化性。

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Electric power grids are evolving into complex cyber-physical power systems(CPPSs)that integrate advanced information and communication technologies(ICTs)but face increasing cyberspace threats and attacks.This study considers CPPS cyberspace security under distributed denial of service(DDoS)attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources.Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks.Different resource allocation strategies are adopted by attackers and defenders under the three cases of attack-free,failed attack,and successful attack,which lead to a corresponding consumption of resources.A multidimensional node value analysis is designed to introduce physical and cybersecurity indices.Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.

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。

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。

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.