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.

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.

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.

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。

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.

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。

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.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

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.

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。

电力系统防御DoS网络攻击的机制研究

传统电力系统容易受到网络干扰和攻击,系统中某一部分受到攻击可能会导致整个电力系统瘫痪。由于现代电力系统的广域性和灵活性会导致出现更多的网络攻击点,因此针对新领域研究更多的防御策略变得至关重要。基于此,利用连续时域模型对各种攻击策略进行建模,并分析电力系统防御拒绝服务(Denial of Service,DoS)攻击的机制。

A Novel Formal Theory for Security Protocol Analysis of Denial of Service Based on Extended Strand Space Model

Denial of Service (DoS) attack,especially Distributed Denial of Service (DDoS) attack,is one of the greatest threats to Internet.Much research has been done for it by now,however,it is always concentrated in the behaviors of the network and can not deal with the problem exactly.In this paper,we start from the security of the protocol,then we propose a novel theory for security protocol analysis of Denial of Service in order to deal with the DoS attack.We first introduce the conception of weighted graph to extend the strand space model,then we extend the penetrator model and define the goal of anti-DoS attack through the conception of the DoS-stop protocol,finally we propose two kinds of DoS test model and erect the novel formal theory for security protocol analysis of Denial of Service.Our new formal theory is applied in two example protocols.It is proved that the Internet key exchange (IKE) easily suffers from the DoS attacks,and the efficient DoS-resistant secure key exchange protocol (JFK) is resistant against DoS attack for the server,respectively.

DoS攻击下一类非线性系统的事件触发自适应NN控制

本文研究了一类未知非线性系统在拒绝服务攻击下的弹性控制问题。首先采用反步法、神经网络逼近和事件触发机制设计事件触发自适应神经网络控制器,并证明所引入的事件触发机制可避免Zeno行为。在上述基础上,考虑拒绝服务攻击的影响,设计弹性事件触发控制器,并利用Lyapunov稳定性理论证明闭环系统在遭受拒绝服务攻击时仍能保持稳定。最后,运用单连杆机械臂系统验证了该控制策略的有效性。

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。

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.