Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

The Advertisement Translation Based on the Prototype Theory

Based on the Prototype Theory,the prototypical feature of advertisement is found to be the combination of three language functions:the informative function,the expressive function,and the vocative function.The advertisement translation means the adjustment of the informative function and the expressive function according to the differences between languages or cultures in order to maximize the vocative function.The faithful translation is the closest to the prototype of the source text but not necessarily the best translation.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

基于K-prototype的精准实践育人研究

实践育人是新时代我国教育方针的重要价值导向,培养什么样的人是教育的首要问题。以新媒体创新思政实践等项目的实时数据通过K-prototype聚类方法,构建以大学生心理画像和问卷测试为基础,大数据技术与高校思政工作相融合的实践路径,精准识别思政工作的对象和所遇到的问题。揭示学生的个体差异和学习特点为教育者提供定制化的教育策略有效预防潜在风险,提升思政工作的针对性和实效性,为未来高校思政工作提供新的思路和方向。

基于K-prototypes的混合属性数据聚类算法改进

属性数据分为数值型数据和分类型数据,一般情况下对于数值型数据运算前要进行标准化处理,但是对于数值型数据差异大的数据,由于大数掩盖小数的影响,按照K-prototypes聚类算法,数值型数据标准化后而且不对相应的分类数据有任何预处理或者在计算时没有进行任何改变,很可能提高分类数据在聚类中的影响,并且分类型数据并未进一步地细分,不能满足不同要求的混合属性聚类。该文在将数值型数据标准化的基础上,将分类数据细分为二元数据和类型数据,并用相异度系数距离计算分类数据之间的距离,并且赋予二元和类型数据相应的权重,来改进K-prototypes聚类算法,使该算法满足不同要求的混合属性数据聚类,最后通过C#语言,在ArcEngine2010版本上实现。

A multi-purpose prototype test system for mechanical behavior of tunnel supporting structure: Development and application

A multi-purpose prototype test system is developed to study the mechanical behavior of tunnel sup-porting structure,including a modular counterforce device,a powerful loading equipment,an advanced intelligent management system and an efficient noncontact deformation measurement system.The functions of the prototype test system are adjustable size and shape of the modular counterforce structure,sufficient load reserve and accurate loading,multi-connection linkage intelligent management,and high-precision and continuously positioned noncontact deformation measurement.The modular counterforce structure is currently the largest in the world,with an outer diameter of 20.5 m,an inner diameter of 16.5 m and a height of 6 m.The case application proves that the prototype test system can reproduce the mechanical behavior of the tunnel lining during load-bearing,deformation and failure processes in detail.

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

Development and preliminary results of a large-pixel two-layer LaBr_(3) Compton camera prototype

Lanthanum bromide(LaBr_(3))crystal has a high energy resolution and time resolution and has been used in Compton cameras(CCs)over the past few decades.However,LaBr_(3) crystal arrays are difficult to process because LaBr_(3) is easy to crack and break;thus,few LaBr_(3)-based CC prototypes have been built.In this study,we designed and fabricated a large-pixel LaBr_(3) CC prototype and evaluated its performance with regard to position,energy,and angular resolution.We used two 10×10 LaBr_(3) crystal arrays with a pixel size of 5 mm×5 mm,silicon photomultipliers(SiPMs),and corresponding decoding circuits to construct our prototype.Additionally,a framework based on a Voronoi diagram and a lookup table was developed for list-mode projection data acquisition.Monte Carlo(MC)simulations based on Geant4 and experiments were conducted to evaluate the performance of our CC prototype.The lateral position resolution was 5 mm,and the maximum deviation in the depth direction was 2.5 and 5 mm for the scatterer and absorber,respectively.The corresponding measured energy resolu-tions were 7.65%and 8.44%,respectively,at 511 keV.The experimental results of ^(137)Cs point-like sources were consistent with the MC simulation results with regard to the spatial positions and full widths at half maximum(FWHMs).The angular resolution of the fabricated prototype was approximately 6°when a point-like ^(137)Cs source was centrally placed at a distance of 5 cm from the scatterer.We proposed and investigated a large-pixel LaBr_(3) CC for the first time and verified its feasibility for use in accurate spatial positioning of radiative sources with a high angular resolution.The proposed CC can satisfy the requirements of radiative source imaging and positioning in the nuclear industry and medical applications.

基于在线梯度下降的Mini Batch K-Prototypes算法

K-Prototypes算法每次迭代都需要花费大量时间来计算所有样本与每个簇中心的相异度以将其划分到各个聚类簇中,这导致K-Prototypes算法在处理大型数据集时运行时间急剧增加。根据在线梯度下降算法对K-Prototypes算法的迭代过程进行优化,减少算法每次迭代所需的计算量从而降低算法的时间复杂度,提升算法运行效率。实验结果表明,提出的基于在线梯度下降的K-Prototypes聚类算法,可以在不影响算法收敛性和有效性的前提下降低算法的时间复杂度,提升算法运行效率。

基于改进K-prototypes与GBDT的城市干道车辆出行群体辨识模型

为了掌握城市干道交通运行规律,向交通管理部门制定相关交通需求管理政策提供理论依据,提出了一种基于组合模型的城市干道车辆出行群体辨识模型。基于青岛市胶州湾隧道过车数据,从出行强度、出行时间与出行习惯3个维度构建了出行特征指标体系以全面刻画车辆个体的出行行为。基于相关性分析剔除了冗余指标以避免对辨识研究的影响。针对混合属性出行特征指标数据,使用改进K-prototypes算法以有效地实现车辆出行群体划分,将其与GBDT算法相结合,建立了一种基于改进K-prototypes与GBDT的辨识模型,随机选取10000个样本开展辨识研究。结果表明:研究道路存在5类车辆出行群体:高频通勤群体、低频通勤群体、营运群体、频次稳定群体与普通群体,对于这5类车辆出行群体,平均识别准确率为97.75%,最高识别准确率可达99.47%。

基于实测数据的半潜式平台贝叶斯模型修正方法研究

随着半潜式平台的长期海上服役,平台的基本设计参数会发生变化,如仍基于原始设计参数对平台进行分析将影响结果的准确性。本文以半潜式平台服役中易发生变化的基本参数(平台排水量、重心高度和三个自由度回转半径)作为修正参数,以平台横摇、纵摇和垂荡自由度的方差和波频功率谱密度构建目标函数,建立可依据实测数据的半潜式平台贝叶斯参数修正方法。以我国某半潜式平台实测数据为例,开展平台的参数修正研究,并基于修正后的参数对平台多年一遇环境荷载下的响应进行对比评估。

“数据”可否纳入知识产权客体范畴?

《民法总则》(草案)第108条将“数据信息”纳入知识产权客体范畴,虽然该规定最终未获通过,但却引发了学界和业界的热议,数据可否纳入知识产权客体范畴的讨论方兴未艾。根据“信息链”理论,事实、数据、信息、知识和情报之间是呈现一种由下至上逐步提炼加工的链式关系,数据与信息、知识、情报之间依次包含。但数据并不符合知识产权客体的一般特征,若将“数据”整体直接纳入知识产权客体的范畴,会冲击已有的权能和体系,影响知识产权客体家族的统一性和协调性。应对数据进行分类保护,在现有知识产权保护基础之上,增设数据产权保护模式,数据主体就个人原始数据享有数据人格权和数据财产权;企业就其加工处理取得的衍生数据享有数据财产权,企业这种财产权利取得的门槛低于知识产权取得,不必要求独创性、创造性或显著性等知识产权客体的一般特征,宜采取弱保护模式。

课堂德育评估的基本路径与范例分析

课堂是教师“立德树人”“学会育人”的重要场域。课堂德育是学校德育的基本形式,它包括“课程育人”和“教学育人”两个方面。如何对教师的课堂德育能力进行有效的评估,一直是个难题,国内现有的相关研究并不多见。文章主要关注和聚焦教学层面的课堂德育及其评估,对国际上近几十年来有关课堂德育评估研究的主要成果、前沿进展及发展趋势进行了介绍与分析,从中概括提炼出4条基本的课堂德育评估路径,即自陈式问卷调查评估、以课堂观察为基础的表现性评估、指向课堂的情境判断测验、促进社会情感学习的课堂氛围评估,并借此对课堂德育评估的关键问题“评什么“”如何评”进行了总结与评析。

双路径合作的原型矫正小样本分类模型

基于度量的元学习在学习过程中存在由于稀缺数据分布导致习得的先验知识不足、从样本中提取到的单一视图特征易受弱相关或无关特征的干扰以及因分类造成的代表性特征偏差的问题。针对这些问题,提出了一种双路径合作的原型矫正小样本分类模型。首先,通过双路径合作模块从多视图角度自适应地突出关键特征和弱化弱相关特征,充分利用特征信息获得先验知识来提升特征的表达能力;其次,通过基于查询集样本特征信息的原型矫正分类策略来解决类内原型的偏差问题;最后,通过损失函数反向更新模型参数,模型分类准确率得以提升。在五个公开的数据集上进行了5-way 1-shot和5-way 5-shot对比实验,较基准模型而言,在miniImageNet数据集上,准确率提升了5.57个百分点和3.90个百分点;在tieredImageNet数据集上,准确率提升了5.68个百分点和3.93个百分点;在CUB数据集上,准确率提升了6.93个百分点和3.13个百分点;在CIFAR-FS数据集上,准确率提升了8.03个百分点和1.65个百分点;在FC-100数据集上,准确率提升了4.25个百分点和4.89个百分点。实验结果表明,提出的双路径合作的原型矫正小样本分类模型能在小样本学习领域有良好的性能,且模型中的模块可迁移到其他模型中使用。

基于微调原型网络的小样本敏感信息识别方法

敏感信息识别主要是指识别互联网上涉及色情、毒品、邪教、暴力等类型的敏感信息,现有的敏感信息识别通常将其看作文本分类任务,但由于缺乏大规模的敏感信息标注数据,分类效果不佳。该文提出一种基于微调原型网络的小样本敏感信息识别方法,在小样本学习框架下,利用快速适应的微调原型网络来缓解元训练阶段通用新闻领域和元测试阶段敏感信息数据差异大的问题。首先,在元训练阶段,基于通用新闻领域的分类数据训练模型来学习通用知识,同时在训练过程中经过两阶段梯度更新,得到一组对新任务敏感的快速适应初始参数,然后在元测试阶段敏感文本数据集的新任务上,冻结模型部分参数并使用支持集进一步微调,使模型更好地泛化到敏感识别领域上。实验结果证明,相比当前最优的小样本分类模型,该文提出的快速适应微调策略的原型网络显著提升了敏感信息识别效果。

考虑气动阻尼影响的海上风电筒型基础整机运行状态振动模拟研究

运行状态下由叶轮旋转效应产生的气动阻尼对海上风电结构振动影响显著,然而当前的数值模拟方法受到计算能力与施加气动阻尼方式的限制,无法真实高效地表征气动力对振动的影响,使得模拟结果与实际工程存在偏差。针对上述问题,以某海上风电场内1台筒型基础整机为研究对象,考虑气动阻尼效应搭建了整机顺风向振动的简化理论模型,研究气动阻尼对运行状态下整机振动的折减效果,并以折减系数形式引入到数值模型计算中再与原型观测结果进行对比。研究表明:所建理论模型与数值模型的一阶自振频率与实测数据反馈值均相差较小,两类模型对实际工程的适用性良好;理论模型与数值模型同风速下的振动差值稳定,两者动力学上具有很好的同步性;停机工况与运行工况下,模拟塔筒顶部振动位移均方根值与原型观测均方根值平均误差分别为12.19%与7.99%,说明基于考虑气动阻尼效应的振动理论模型对数值模拟结果进行折减是可行的,模拟精度可以满足工程需求。

采用特征图增强原型的小样本图像分类方法

在基于度量学习的小样本图像分类方法中,由于标注样本的稀缺,仅用支持集样本得到的类原型往往难以代表整个类别的真实分布;同时,同类样本间也可能在多个方面存在较大差异,较大的类内差异可能使样本特征偏离类别中心。针对上述可能严重影响图像分类性能的问题,提出一种采用特征图增强原型的小样本图像分类方法(FMEP)。首先,用余弦相似度从查询集样本特征图中选择部分相似特征加入类原型中,得到更具代表性的特征图增强原型;其次,对相似的查询集样本特征进行聚合,缓解类内差异大导致的问题,使同类样本的特征分布更接近;最后,用在特征空间中与真实类别分布都更接近的特征图增强原型和聚合查询特征进行相似度比较得到更优的分类结果。所提方法在MiniImageNet、TieredImageNet、CUB-200和CIFAR-FS等常用的小样本图像分类数据集上进行了实验,结果表明所提方法获得了比基线模型更优的分类性能,同时也优于同类型的小样本图像分类方法。

液压与气压传动综合探究性实验的设计

为解决传统实验教学过程中学生高阶能力培养不足的问题,设计了基于液压挖掘机的综合探究性实验。利用虚拟现实技术建立了液压挖掘机的虚拟样机,采用Multi-system 5060手持式液压测试仪、压力传感器、流量传感器等对挖掘机动作过程中的压力、流量、发动机转速进行实测。根据实测数据分析了执行元件的动作性能、压力冲击特性、压力损失特性等,完成了相关实验资源的建设,为引导学生设计及分析液压系统提供了理论和实测依据。在此基础上提出了实验实施方案,将实验分解为设备工况调研分析、回路设计、系统设计、实操优化4个环节,引导学生以小组协作的方式探究并解决真实的工程问题。

带定位榫盾构隧道管片接头剪切力学行为研究

外套于盾构隧道管片螺栓的空心圆锥筒定位抗剪构件定位榫能有效控制盾构管片错台量和错台值,与管片、螺栓共同形成“管片-定位榫-弯螺栓”接头.为探明盾构隧道管片受压工况、“管片-定位榫-弯螺栓”接头在剪切荷载作用下的结构力学特征和破坏机理,以内径7.5 m、外径8.3 m、弯螺栓连接的盾构隧道衬砌环的纵向接头为例,首先,建立三维有限元模型,对轴力分别为196、392、588和784 kN4种工况的接头剪切破坏全过程进行数值模拟;然后,对接头剪切破坏过程、定位榫和螺栓力学及变形特征、管片接缝面变形破坏特征进行分析;最后,采用原型剪切破坏试验对数值模拟结果进行印证和补充研究.研究结果表明:盾构隧道“管片-定位榫-弯螺栓”接头剪切破坏全过程分为接触面静摩擦、定位榫弹性变形、定位榫塑性错动和螺栓剪切破坏4个阶段;定位榫弹性变形阶段,定位榫表现为“压扁”形态,定位榫塑性错动阶段,定位榫拱顶外侧首先出现塑性屈服区,随着剪切荷载的增加,塑性区将扩展到定位榫端部和横断面内大部分区域,塑性破损过程中定位榫将超过其承载能力的外部剪切荷载转移给螺栓;螺栓杆体受力从定位榫塑性错动阶段开始,最终加载侧杆体将发生“拉直”破坏、固定侧杆体将发生弯剪破坏;管片接缝面混凝土从定位榫塑性错动阶段后期出现塑性区,初始位于榫槽内水平两侧,随着剪力增加,塑性区将向周边扩展,最终与管片内弧面手孔塑性区连通,形成“手孔-螺栓孔-手孔”塑性破坏域.