Reckoning with COVID-19 denial: Brazil's exemplary model for global response

In the aftermath of the coronavirus disease 2019(COVID-19)pandemic in Brazil,accountability is crucial for those who denied the severity of the virus,spreading false information and causing harm.Some individuals have already faced legal proceedings against them,revealing economic motivations behind their actions.It is equally important to hold doctors accountable for prescribing ineffective treatments,putting the population at risk.The leaders of the denial movement and the federal government,who mishandled the pandemic,should be held accountable for the high death toll.Seeking justice from the legislative and executive branches is necessary,along with exemplary measures for those who spread misinformation about COVID-19.

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.

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.

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.

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。

GNSS拒止时基于并行CNN-BiLSTM回归和残差补偿的UAV导航误差校正方法

全球导航卫星系统(GNSS)拒止时,GNSS/惯性导航系统(INS)组合导航系统的性能严重下降,导致无人机集群导航误差快速发散.目前,利用神经网络预测位置与速度代替GNSS导航信息可校正无人机INS误差,但该方法仍存在定位误差较高且在轨迹突变时预测精度急剧下降的问题.因此,提出了一种基于卷积-双向长短时记忆网络联合残差补偿的位置与速度预测方法,用于提高位置与速度预测精度.首先,针对GNSS拒止后GNSS/INS组合导航系统定位误差较高的问题,提出卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)的融合模型,该模型可建立惯性测量单元(IMU)动力学测量数据与GNSS导航信息之间的关系,实现较准确的位置和速度预测.其次,针对轨迹突变时预测效果急剧下降的问题,提出并行CNNBiLSTM回归架构,在预测位置与速度的同时,挖掘IMU动力学测量数据、预测值与预测残差之间的关系,预测并补偿预测残差,增强模型在轨迹突变时的预测精度.仿真结果表明,所提模型在预测准确性、有效性和稳定性方面都优于CNN-LSTM、LSTM网络模型.

战略文化视阈下美国对太平洋岛国的策略与路径

第二次世界大战后,美国与太平洋岛国在互动的过程中形成了以“美国例外”“有限干预”和“战略拒止”为特征的战略文化。基于“美国例外”,美国提出需保持对“自由联系国”的“绝对权威”;基于“有限干预”,美国寻求与太平洋岛国建立机制化合作关系;基于“战略拒止”,美国对域外国家介入太平洋岛国事务保持高度警惕。在战略文化的驱动下,美国积极改善与南太平洋区域组织的政治外交关系,加强顶层设计,由美国国际开发署统筹对太平洋岛国的对外援助事务,开展公共外交活动树立美国正面积极的国际形象,并将太平洋岛国纳入伙伴关系网络,在太平洋岛国及周边地区扩大军事部署。即便如此,由于美国对太平洋岛国的发展问题缺乏认知,忽视双方历史、文化差异并与太平洋岛国存在现实利益分歧,美国在太平洋岛国的战略文化辐射效果有限。

关联公司法人人格否认对实际控制人扩张适用之研究

司法实践中,实际控制人损害债权人利益现象频现,而《公司法》未将实际控制人纳入法人人格否认制度的规制范围,此类案件裁判工作陷入实际控制人责任形态缺失、对其滥用行为未实现整体规制以及责任认定与立法不统一的窘境。实际控制人适用关联公司法人人格否认制度契合法人制度的本质,能够保护债权人利益,堵住法律漏洞,有效规制实际控制人的滥用行为。未来《公司法》应将实际控制人纳入关联公司法人人格否认制度规制范围,完善法人人格否认制度,从立法目的出发,扩张解释现行《公司法》第20条第3款,对实际控制人扩张适用关联公司法人人格否认制度;法院审理此类案件时应严格把握构成要件,采用多重标准认定行为要件,重视结果要件;法院应坚持审慎立场,考察适用必要性,坚持“一案一否认”,防止法人人格否认制度被滥用。

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。

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。

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CNN的5G网络切片安全分配研究

网络切片是5G网络的关键技术,在支持多种5G应用和服务方面发挥着重要作用。为确保5G网络提供更加灵活安全的按需服务,对网络切片的灵活性和安全性研究尤为重要。为此,提出一种按应用服务类型划分细粒度网络切片的方案,并通过基于卷积神经网络(Convolutional Neural Networks,CNN)的模型来安全分配网络切片。当网络流通过该模型后,先筛选出受到分布式拒绝服务(Distributed Denial of Service,DDoS)攻击的流量,然后良性流量再按应用类型分配到相应的切片上。仿真结果表明,基于CNN的网络切片分配模型,在安全分配网络切片方面有着显著的效果。与其它常见的机器学习分类算法相比,该方案中的模型在准确率、精确率、召回率和F1分数方面都有着更好的性能优势。

一种满足否定约束的增量数据修复算法

为了提高数据质量,数据修复技术得到了广泛的研究。数据修复通常使用约束,常用的有函数依赖、条件函数依赖和否定约束(Denial Constraints, DCs)等,其中,否定约束具有足够的表达能力,可以包含许多其他依赖项,因此在数据修复中得到了很好的应用。另外,数据通常是动态的,因此需要增量修复技术,以响应数据更新。该文提出一种满足否定约束的增量修复算法,结合辅助的索引结构,能有效地识别增量数据引起的DC冲突。大量的实验结果表明了该方法的有效性和高效性。

基于引导优化的拒止环境无人系统相对定位

在卫星拒止条件下,包含无人机、无人艇等多个节点的无人系统可以基于节点间距离信息,使用相对定位方法解算所有节点的绝对地理位置。由于无人系统节点的高机动特性,节点间的距离信息具有瞬时有效性,因此需要实现快速、精确的相对定位解算。旋转矩阵的计算是相对定位解算的核心步骤,使用传统的最小二乘法、牛顿法、遗传法等无法兼顾效率与精度因素,基于自由度松弛的引导优化算法通过放松旋转矩阵的自由度能够将寻优参数的初始值一步引导至真值邻域附近,再利用迭代法进行精细寻优,从而实现旋转矩阵的短时间、高精度计算。仿真实验结果表明,在无人系统测距误差达到5 m时,基于引导优化的相对定位方法解算时间为0.014 s,产生的定位误差为25.73 m,与牛顿法的解算精度相当,同时减少了55.56%的计算时间;与最小二乘法的计算时间相当,同时减少了39.47%的定位误差。

一种机载惯性/光电组合导航误差修正方法

为提升无人机在全球卫星导航系统受限甚至拒止环境下的导航精度,提出了一种机载惯性/光电组合导航误差修正方法。考虑了捷联惯性导航系统(SINS)姿态误差对光电探测系统(EODS)定位精度的影响,建立了包含SINS姿态误差的测量方程,推导了滤波观测噪声与EODS测距、测角误差之间的函数关系,建立了准确的观测噪声协方差矩阵模型。仿真结果表明:相较于传统SINS/EODS组合导航算法,所提方法滤波收敛速度更快,并且有着更高的姿态和位置精度,2000 s后横滚、俯仰、航向精度分别提高47%、59%、82%,东向、北向、天向定位精度分别提高49%、44%、25%,提升了无人机在卫星信号较弱或缺失的环境下的导航能力。

基于巡检机器人的吊装作业场景DPIM算法

为提高吊车安全作业的高精度检测预警,增强企业安全管理能力,围绕工业安全事件分析与监测预警无人化的需求,定制吊装场景地面和空中飞行相结合的巡检机器人,智能化吊装过程安全规程监控、弹窗图像记录和安全告警。首先,制作包含3120张图片的吊装数据集Cranes-Dataset(CRN-Dataset),提出一种动态视角智能监测(DPIM)算法,以增强人-车-物多尺度目标的快速检测能力;然后,依据多帧图像的角点检测和带噪声基于密度的聚类方法,以及吊车与作业工人空间距离的安全属性,制定安全规则触发告警的流程,实时记录违规操作图像并弹窗预警。结果表明:经过实际部署和验证,DPIM算法相较于其他传统算法,吊装作业目标识别能力有明显提高,且适用嵌入式边缘智能分析节点的实时计算与数据传输,完成危险区域人员拒止的现场部署。

列车控制系统的抗拒绝服务攻击弹性控制策略

当遭受拒绝服务(DoS)攻击时,分布式列车控制系统的弹性控制问题受到广泛关注.本文提出了一种基于分布式领导车状态观测器和障碍李雅普诺夫函数的弹性控制策略,不仅可以避免列车碰撞,同时实现了编队控制的目标.首先,给出了一种分布式的领导车状态观测器设计方法,用于实时估计领导车的状态.理论分析表明,在DoS攻击满足一定约束的条件下,该状态观测器的估计误差具有指数稳定特性.在此基础上,通过将列车碰撞避免问题转化为状态受限问题,提出一种基于障碍李雅普诺夫函数的状态受限控制律,解决了DoS攻击下确保碰撞避免的车队控制问题.最后,数值仿真证实了本文方法的有效性.

测度、反诘与让步——中古汉语“岂”的否定功能

中古汉语里“岂”是一个常用的语气副词,其否定辖域分为跨短语和跨句两类。“岂”一般用于谓词之前,在疑问句和陈述句中引领句末的语气词通过是非疑问句、反问句和让步复句三种句式来表达对命题行为、认识、言语三域的否定;它的否定主要聚焦在对命题的目的、结果和原因的主观评价,具有顺转引领、逆转总结、双向过渡三种功能。本文主要通过对魏晋时期文献的考察,来探析否定标记“岂”在中古汉语里的使用情况及存在条件,并进一步确立它的否定来源和历史成因。