CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Analysis and Programming of Kernel for Embedded Systems

In this study, we present a miniOS kernel implemented via analysis of the context switching, the scheduler, and the memory management of the original OS kernel for an embedded system based on ARM core. Since this is a large subject, we have limited our scope to them only that made up an embedded operating system. The implemented miniOS kernel is composed only by them, to the exclusion of all other functions of the original kernel. Our goal is to modify the OS kernel depending on the product function. The implementation method of the miniOS kernel can be applicable to any OS being mounted based on the ARM core. Modifying the kernel depending on the product function can improve the OS booting speed as well as save the system memory. The functions of the scheduler, the context switching, and the memory management are described with the source in each section. The miniOS kernel was implemented in the Assembly and C language and was verified through the build and the test. The results are shown in the Section 5.

KERNEL NEIGHBORHOOD PRESERVING EMBEDDING FOR CLASSIFICATION

The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.

Embedded Linux Device Driver Research

Based on the in-depth research of the embedded device management system,analyze the process of the driver parameters delivery on kernel level particularly.According to this,expound the device driver 's working principle,structure and design methods especially.Finally realize the drivers of the character-based devices which can be dynamically loaded.Actual result shows that mastering the realization of the device driver mechanism and the process of parameters delivery with kernel can improve the embedded device driver development efficiency and reduce error probability effectively,thus saving the development cost and development cycle of embedded products.

Visualization of protein structure relationships using constrained twin kernel embedding

In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.

基于神经切线核的学件RKME规约

当前机器学习技术已经在大量领域得到广泛应用,然而仍面临许多亟待解决的问题:依赖大量的训练数据和训练技巧、难以适应环境变化、数据隐私/所有权的保护、灾难性遗忘等等.最近,学件范式使得上述问题同时得到系统性地解决成为可能.在该范式下,用户面临新的机器学习任务时可以通过学件基座系统方便地复用他人的结果,而不必从头开始.学件范式的核心在于规约,规约使得学件基座系统在不接触原始数据的情况下,可以根据用户的需求快速识别出对用户任务有帮助的学件.近期研究均通过缩略核均值嵌入(Reduced Kernel Mean Embedding,RKME)为模型构造规约,并通过构建学件原型系统验证了范式的有效性.在实际中,学件基座系统中往往包含在各种领域任务、数据类型上构建的机器学习模型,而传统的RKME规约面临维度灾难的问题,难以适用于高维数据,例如图像场景.为了拓展RKME规约的适用范围,本文引入神经切线核进行RKME规约构造.为提升方法的高效性,本文进一步通过神经网络高斯过程与随机特征近似,快速为各种模型生成RKME规约.最后,本文在真实数据构建的销量预测、图像分类场景的学件基座系统中进行大量实验验证了所提出方法的有效性和高效性,所提出方法相比于传统RKME规约查搜准确率显著提升近9%,且实验结果表明改进后的规约在图像任务上具有良好的隐私保护性质.代码见:.

T-Kernel任务调度的实时性分析

T-Kernel由于其实时性及开源性,在全球微处理器操作系统市场中已占据了一半以上的份额。该文分析了T-Kernel的任务状态及任务调度机制,给出了其调度时机及调度算法,并从嵌入式实时操作系统的实时性出发,分析了该调度机制的主要优点。

基于T-Kernel的POSIX线程库的研究

在分析了T-Kernel对POSIX标准接口支持的基础上,结合MiniGUI在嵌入式操作系统T-Kernel上的移植,引入了任务与线程一一映射模式,实现了满足实时应用的基于T-Kernel的POSIX线程库。通过运行MiniGUI的应用程序,验证了其可以满足程序并发执行的要求。

嵌入式操作系统Visual DSP++Kernel的原理与应用

本文剖析了ADSP中基于嵌入式操作系统Visual DSP++Kernel(VDK)开发工具的原理与应用、优点、常用库函数、DEBUG工具以及它的具体应用。通过对VDK的学习,可以使学生进一步理解和掌握嵌入式操作系统的工作原理,提高他们的应用和开发能力。

Little Kernel分析与移植

Little Kernel(lk)是被Android系统接受进入源码树的Bootloader程序,并被多款智能手机和平板电脑所采用。论文介绍了lk的主要功能,分析了lk的源码结构,并在此基础上详细说明了lk移植的方法和过程。将移植后的lk进行编译并下载至TCC8801 DEMO板上,lk能够正常启动并引导linux内核。

基于KCF和双目测距的目标跟随系统设计与实现

针对KCF(核相关滤波算法)对快速运动过程中的物体或在视频帧率较低情况下,稳定性较差的问题。论文在经典KCF算法上结合了双目测距算法,设计并实现了一种目标跟随系统。实验表明,结合KCF和双目测距的算法不仅能够有效提升跟随效果并且满足嵌入式平台的运行要求。

用GNU工具链实现μCOS到ARM平台的移植

文章分析了在ARM平台上使用GNU工具链实现μCOS嵌入式内核移植的技术要点;阐述了移植的关键步骤,以及在ARM平台上移植的具体实现方法.内容包括操作系统引导及装载程序的设计、μCOS运行之前硬件环境的建立、μCOS与平台相关接口程序、串口通信程序及相关辅助性实用程序的设计等.

μCOS移植技术探究

文中以X86平台及ARM平台为例,分析了μCOS嵌入式内核移植的技术要点.内容包括X86平台及ARM平台硬件环境的简述、操作系统引导及装载程序的设计、μCOS运行之前硬件环境的建立、μCOS与平台相关接口程序、设备驱动程序、实用库函数的设计等.

基于μC/OS-Ⅱ与T-Kernel的RTOS比较分析

实时操作系统在嵌入式系统中占有重要地位,它是嵌入式应用程序开发的基础平台。目前市场上的RTOS种类繁多,选择使用时存在一定难度,因此,将介绍两款应用前景广阔的嵌入式实时内核μC/OS-Ⅱ与T-Kernel,根据二者主要性能指标做出详细的比较分析,并给出各自的适用领域,为嵌入式系统设计者提供参考。

基于T-Kernel的CF卡驱动程序设计

介绍了T-Kernel的两种设备驱动程序接口及相关函数,解析和设计T-Kernel的CF卡驱动开发流程。

eCos实时内核分析

简要介绍了嵌入式实时操作系统eCos的实时性能特点,重点分析了eCos的中断处理机制、任务调度器结构和实时性能测试数据,提出了eCos适用范围的看法。

μCOS+EFSL文件系统移植研究与实现

文章分析了在X86平台保护方式下使用GNU工具链实现μCOS+EFSL文件系统移植的技术要点;阐述了移植的关键步骤,以及在X86平台移植的具体实现方法.内容包括μCOS及EFSL文件系统的移植要点、EFSL RAM盘的创建、EFSL磁盘接口函数的设计与实现等.

大数据环境下嵌入式可信软件异常识别研究

嵌入式多任务操作内核是其应用的核心,但由于嵌入式操作具有开放性,导致网络化软件中出现风险漏洞,引发可信软件异常问题,对操作内核造成损坏。大数据环境下软件数据的大规模和高复杂度加大了软件异常识别的难度,为此,提出嵌入式操作内核可信软件异常识别方法。结合不同类型的受限玻尔兹曼机,构建深度信念网络用于数据降维。采用梯度下降法改进遗传算法,将其应用在支持向量机的优化中。通过优化的支持向量机建立软件异常识别模型,将降维后数据输入模型,完成嵌入式操作内核可信软件异常识别。实验结果表明,所提方法下软件异常识别的准确率、召回率、查全率和F1值均在95%以上,且有效降低了内存开销和性能开销。

玉米籽粒直收机夹带损失检测系统设计与试验

针对目前玉米籽粒直收机籽粒损失检测系统缺乏检测夹带损失技术的问题,设计了基于嵌入式单片机的玉米籽粒直收机夹带损失检测系统。该检测系统包括损失检测传感器、数据采集器和数据显示终端,可以同时监测收获机的清选损失和夹带损失,实时反馈收获机的收获损失速率以及损失量。通过模态仿真软件对不同材料、不同厚度的监测板进行有限元分析,选择厚0.5 mm的不锈钢板作为监测板;运用Multisim对滤波器性能进行仿真分析;设计基于STM32系列单片机的自适应限时滤波算法,可以有效抑制谷物撞击引起的的余振干扰。在试验台架上,对不同大小的玉米籽粒、杂余以及玉米穗进行标定试验,获取信号特征;经装机试验表明,玉米籽粒夹带损失检测结果最大误差为9.96%,平均误差约为6.52%,损失速率变化趋势反馈及时,能够辅助工作人员进行作业决策。

基于模拟串行端口的虚拟设备通信技术研究

MBSE是复杂系统设计的重要范式,尤其对于大型嵌入式系统设计具有重大意义;但在MBSE的虚拟验证环节,针对节点的串行端口的互联通信仍缺乏有效灵活的手段;为解决该问题,提出了一种基于Linux系统内核驱动的串行端口模拟方式,实现了基于模拟串行端口的多虚拟设备间的通信技术;分析了标准串行端口的内核驱动架构,进行规范的驱动设计,构建模拟串行端口,以模拟设备替代真实硬件设备;实验验证了模拟的串行端口的功能可用性,且具备平均456.98 Mbps的最大传输速率,满足物理串行端口的速率范围要求;利用该模拟串行端口,实现了虚拟设备间的双向串行通信实验,能够支持嵌入式子系统间串行通信模拟的需要。