A Practical Study of Big Data Technology in Computer Network Information Security Processing

In recent years,China has witnessed continuous development and progress in its scientific and technological landscape,with widespread utilization of computer networks.Concurrently,issues related to computer network information security,such as information leakage and virus invasions,have become increasingly prominent.Consequently,there is a pressing need for the implementation of effective network security measures.This paper aims to provide a comprehensive summary and analysis of the challenges associated with computer network information security processing.It delves into the core concepts and characteristics of big data technology,exploring its potential as a solution.The study further scrutinizes the application strategy of big data technology in addressing the aforementioned security issues within computer networks.The insights presented in this paper are intended to serve as a valuable reference for individuals involved in the relevant fields,offering guidance on effective approaches to enhance computer network information security through the application of big data technology.

Analysis on The Application of Data Mining Technology in Computer Network Virus Defense

With the rapid development of computer network,the society has entered the information and digital era,it plays an important role in speeding up the pace of social development and providing more convenient services for people.However, the security problem of computer network is becoming more and more serious. All kinds of network viruses pose a great threat to the security of computer network.As the most advanced data processing technology currently, data mining technology can effectively resist the invasion of network virus to computer system,and plays an important role in improving the security of the computer network.This paper starts with the concept of data mining technology and the characteristics of computer network virus,and makes an in-depth analysis on the specific application of data mining technology in the computer network virus defense.

Research on the Application of Big Data and Artificial Intelligence Technology in Computer Network

With the continuous development of social economy,science and technology are also in continuous progress,relying on the Internet technology of big data era has come in an all-round way.On the basis of the development of cloud computing and Internet technology,artificial intelligence technology has emerged as the times require.It also has more advantages.Applying it to computer network technology can effectively improve the data processing efficiency and quality of computer network technology,and improve the convenience for people’s life and production.This paper studies and analyzes the practical application requirements of computer network,and discusses the application characteristics and timeliness of artificial intelligence technology.

Application Strategy of Data Encryption Technology in Computer Network Security

In the context of the information age,on the basis of the convenience of computer networks,security issues have gradually emerged.The data encryption technology is reasonably applied in the process of computer network security practice,which promotes the safe and reliable operation of the computer network to a certain extent.Based on this,our article regards data encryption technology as the main research object,focusing on its specific application in computer network security.

Research on the Big Data Cloud Computing Based on the Network Data Mining

The big data cloud computing is a new computing mode,which integrates the distributed processing,the parallel processing,the network computing,the virtualization technology,the load balancing and other network technologies.Under the operation of the big data cloud computing system,the computing resources can be distributed in a resource pool composed of a large number of the computers,allowing users to connect with the remote computer systems according to their own data information needs.

Multi-Radio Cooperation Technology and Heterogeneous Network Convergence

The heterogeneous network convergence is the trend of future network development. However, many bottlenecks exist in the converged system such as high time delay, high energy consumption, and low data rate. Multi-Radio Cooperation (MRC) technology is specially designed to overcome these bottlenecks and to satisfy the requirements of heterogeneous network convergence. MRC can improve network capacity, reduce the energy consumption of radio devices, and decrease handover latency between heterogeneous networks by the cooperation of multiple radios, and by efficient management and feasible allocation of multi-radio resources. MRC makes real seamless heterogeneous network convergence possible.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

UPLC-QTOF/MS结合网络药理学探讨诃子与草乌配伍“异病同治”类风湿性关节炎和痛风性关节炎作用机制

目的:探讨诃子与草乌配伍“异病同治”类风湿性关节炎(RA)和痛风性关节炎(GA)的作用机制。方法:采用数据挖掘技术找出治疗“图赖”病的核心药物组合(诃子-草乌),并使用超高效液相色谱-四极杆飞行时间质谱法(UPLC-QTOF/MS)检测大鼠灌胃诃子与草乌配伍药物后的入血成分;运用PubChem、Swiss Target Prediction、GeneCards、OMIM、DisGeNET、Metascape等多个数据库检索诃子与草乌化学成分对应靶点、RA和GA的疾病靶点,以及代谢通路等信息,利用Cytoscape3.8.2软件构建成分-靶点-通路-疾病网络,并利用分子对接方法证实诃子与草乌的主要成分与关键靶点之间的高亲合力。结果:诃子与草乌入血成分相应靶点与RA靶点交集后得到323个交集靶点、与GA靶点交集后得到24个交集靶点,两者共有靶点24个,主要有肿瘤坏死因子(TNF)、肿瘤蛋白p53(TP53)、基质金属蛋白酶-9(MMP-9)、前列腺素内过氧化物合酶2(PTGS2)和信号转导和转录激活因子3(STAT3),与十八碳四烯酸、绢酸、5-(3-羟基-4-乙酰氧基-1-丁基)-2,2'-联噻吩、灵芝酸H等有紧密联系,涉及通路有TNF信号通路、破骨细胞分化通路、癌症通路等。分子对接结果显示灵芝酸H和TNF、5-(3-羟基-4-乙酰氧基-1-丁基)-2,2'-联噻吩和MMP-9有强烈对接活性。结论:蒙古族药诃子与草乌配伍治疗RA和GA具有多靶点效应,可能通过炎症反应的调节、细胞对生物刺激的反应、对细菌源分子的反应、磷酸盐代谢的正调控和对激素的反应等通路发挥“异病同治”作用。

Adaptive security control of time-varying constraints nonlinear cyber-physical systems with false data injection attacks

In this article,an adaptive security control scheme is presented for cyber-physical systems(CPSs)suffering from false data injection(FDI)attacks and time-varying state constraints.Firstly,an adaptive bound estimation mechanism is introduced in the backstepping control design to mitigate the effect of FDI attacks.Secondly,to solve the unknown sign time-varying statefeedback gains aroused by the FDI attacks,a type of Nussbaum function is employed in the adaptive security control.Then,by constructing a barrier Lyapunov function,it can be ensured that all signals of controlled system are bounded and the time-varying state constraints are not transgressed.Finally,the provided simulation examples demonstrate the effectiveness of the proposed controller.

Cluster-based Intrusion Detection in Wireless Ad-Hoc Networks

There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The intrusion detection and cluster-based Ad-Hoc networks has been introduced,then,an architecture for better intrusion detection based on cluster using Data Mining in wireless Ad -Hoc networks has been shown.A statistical anomaly detection approach has been used.The anomaly detection and trace analysis have been done locally in each node and possibly through cooperation with clusterhead detection in the network.

基于大数据技术的网络安全态势感知平台设计

在网络安全威胁不断演变的背景下,构建一套基于大数据技术的网络安全态势感知平台至关重要。本文介绍了网络安全态势感知平台的设计框架,包括系统架构、数据采集与处理、安全事件检测与分析以及实时可视化等关键模块。通过应用案例分析,突显平台在实际环境中的应用效果。实时可视化与报告和自动化响应与持续改进两个章节详细探讨了用户界面设计、报告定制化和自动响应机制。最后,总结了平台设计的关键要点,并展望了未来网络安全领域的发展趋势。

基于BP神经网络模型的矿井生产预警技术研究

矿井生产具有高动态、快时变、混沌和随机特点,生产过程中的人、设备、环境、管理等海量数据存在多源异构特征使得安全风险评估不确定性增加。使用智能算法对空间生产场景进行模拟建模,对影响矿井安全的因素建立预警指标体系并对指标量化与预处理,通过BP神经网络模型对预处理后的指标数据模型训练与测试,测试结果表明,随着迭代次数的增加,模型的收敛性与准确率极大提高,可以最大限度地提供矿井安全风险评估决策所用信息、为提高煤矿安全监测的效率和监测深度提供技术支持。

东南亚恐怖主义网络发动的特征及运行机制研究

当前,东南亚恐怖主义的发展态势呈现出超民族、超国家及超地域的新特点,也出现了跨境犯罪、武装贩毒、人口走私、网络宣传与策划的新动向。在国际恐怖组织利用互联网和自媒体等媒介实施网络发动的新趋势下,东南亚地区更是成为恐怖主义网络发动的主战场之一。恐怖主义网络发动是指恐怖分子利用网络技术进行宣传、招募、组织培训和策动恐怖袭击等活动,可划分为互联网1.0时代的网络破坏、互联网2.0时代的社交媒体利用以及互联网3.0时代的新技术应用。东南亚地区的恐怖主义网络发动具有模仿国际恐怖组织模式、直接进入手机端与高度伪装性等特征。文章深入分析东南亚恐怖主义网络发动的环境、参与主体、驱动力和机制,构建恐怖主义网络发动的过程框架,指出东南亚恐怖主义网络发动研究的重点方向,包括解构典型案例和寻找规律、开发获取和处理关键反恐技术、构建动态监测与预警体系以及建立数据应用平台等。

基于专利异构数据融合的技术演化路径识别方法

[研究目的]针对目前技术演化分析中多关注专利文本,忽略专利引文信息的问题,提出一种基于专利异构数据融合的技术演化路径识别方法。[研究方法]首先,使用Sentence-BERT模型提取专利文本语义特征;其次,使用图卷积神经网络模型将文本语义特征与引文结构特征融合,实现异构数据融合构建专利向量;最后,划分时间窗,使用k-means算法对各时间窗进行技术主题聚类,基于相邻时间窗技术主题相似度构建技术演化路径。[研究结论]以人工智能领域为例进行实证研究,共发现4条技术演化路径。与相关权威报告进行比对,结果表明识别结果与人工智能技术领域的发展现状一致,验证了模型的有效性和科学性。

考虑交通流量俘获的电动汽车充电负荷预测和充电站规划

针对电动汽车(EV)的充电需求,考虑路径的交通流量,以最大交通流量俘获、最小配电系统网络损耗和最小节点电压偏移为目标,构建了一个多目标决策模型对EV充电站进行规划。运用网络扩展技术确定交通流量俘获路径;运用蒙特卡罗模拟算法,确定规划区内EV的最大充电负荷,从而推算得到充电站的容量;运用超效率数据包络分析评价方法,确定经过归一化处理后各目标函数的权重系数,从而将多目标优化问题转化为单目标优化问题,并采用改进的二进制粒子群优化算法进行求解。以一个包含25个节点的交通网络耦合33节点配电系统为算例进行仿真,验证所建模型和所提方法的有效性,并进一步分析EV最大行驶里程、充电站负荷接入不同节点以及不同时刻对各目标函数的影响。

数据安全防护体系在高校校园网中的应用

在高校网络的基本组成结构中,校园网具有保障高校数据信息得以安全稳定传输的重要作用,校园网目前已成为高校师生赖以实现沟通联系的基础设施支撑。目前在网络技术的快速转型以及发展趋势作用下,高校校园网日益存在更加突出的网络数据安全威胁,进而体现全面防范以及科学预测校园网数据安全隐患的必要性。文章主要探讨高校校园网构建数据安全防护体系的实践价值,合理改进校园网现有的数据传输安全保障机制。

基于大数据的计算机网络安全技术探讨

随着互联网的普及和大数据技术的发展,计算机网络安全问题日益凸显。该文探讨了大数据背景下计算机网络安全技术的应用,以及这些技术在提升网络安全管理水平方面的作用,首先概述了大数据背景下计算机网络安全技术的特点及面临的威胁,接着强调了网络安全管理的重要性,并详细介绍了几种主要的大数据安全应用技术。通过分析可知,这些技术为提高计算机网络安全水平、防范网络攻击提供了有效的手段。

基于改进SPRINT分类算法的数据挖掘模型

为解决目前数据挖掘模型分类时间长、挖掘准确率不高的问题,提出基于改进决策树分类算法(SPRINT)的数据挖掘模型。先采用最大-最小规范化公式完成原始数据线性变换,利用改进后的SPRINT分类算法按照输入数据特性进行分类,使用协同过滤技术生成与数据相近的属性集,计算数据属性相似度,生成语义规则集,为用户提供更优的数据服务。选取某公司营销数据集作为样本进行对比实验,结果表明,与对比模型相比,所提出的数据挖掘模型分类时间更短,挖掘准确率更高,能为用户提供更优质的数据服务。

基于区块链技术的通信网络数据安全性增强方法研究

随着信息技术的快速发展,通信网络已成为现代社会不可或缺的一部分,如何保障通信网络数据安全成为一个亟待解决的问题。区块链技术凭借其独特的去中心化、数据不可篡改性及透明性特点,提供了一种新的思路来增强通信网络的数据安全性。文章通过深入分析区块链技术的原理和特性,提出一种基于区块链技术的通信网络数据安全性增强方法。通过实验验证,该方法能够提供更好的安全保障和更高的成本效益。

数据加密技术在计算机网络安全中的应用

在经历了互联网快速普及和几次大的互联网浪潮等重大变革后,计算机网络安全问题已经成为无法忽视的问题。网络攻击、数据泄露等风险事件频繁发生,个人隐私和企业信息都面临着前所未有的威胁。于此背景下,数据加密技术,作为保护网络安全的一把锐利武器,其重要性不言而喻。基于此,本文首先阐述数据加密技术的基本原理,其次阐述数据加密技术在计算机网络安全中的应用优势,最后提出一些数据加密技术在计算机网络安全中的应用策略,以供参考。