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IDS

IDS的相关文献在1988年到2023年内共计741篇,主要集中在自动化技术、计算机技术、无线电电子学、电信技术、经济计划与管理 等领域,其中期刊论文683篇、会议论文15篇、专利文献43篇;相关期刊333种,包括信息安全与通信保密、信息网络安全、电脑知识与技术等; 相关会议14种,包括中国电机工程学会农村电气化分会自动化专委会2008年年会暨学术研讨会、第二十四届中国数据库学术会议、第二届江苏计算机大会等;IDS的相关文献由1000位作者贡献,包括江晋、胡昌振、赵旭等。

IDS—发文量

期刊论文>

论文:683 占比:92.17%

会议论文>

论文:15 占比:2.02%

专利文献>

论文:43 占比:5.80%

总计:741篇

IDS—发文趋势图

IDS

-研究学者

  • 江晋
  • 胡昌振
  • 赵旭
  • 刘寿强
  • 张杰
  • 牛承珍
  • 王卫
  • 苏宪利
  • 厉剑
  • 孙红娜
  • 期刊论文
  • 会议论文
  • 专利文献

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    • 周宏林; 蒲晓珉; 李勇; 赵志海; 李融; 杨志伟; 刘迎; 潘轶凡
    • 摘要: 全球主要国家积极开展工业数据空间研究的背景下,本文首先介绍了工业数据空间的概念内涵和4个典型应用场景,其次基于应用和需求,介绍了工业数据空间的整体架构,然后从业务、数据与服务、软件及安全等技术层面阐述了工业数据空间的参考架构模型,最后基于东方电气集团内部数据模型传输的场景,搭建了测试床,验证了系统部署的可行性。
    • 东方电气评论编辑部
    • 摘要: 尊敬的各位读者:《东方电气评论》第1期第1页文章标题《某燃料工业数据空间(IDS)技术概述及其测试床部署实践》应为《工业数据空间(IDS)技术概述及其测试床部署实践》,特此更正,深表歉意!谢谢大家对《东方电气评论》的关注与支持!
    • Ali Altalbe; Faris Kateb
    • 摘要: Purpose-Virtually unlimited amounts of data collection by cybersecurity systems put people at risk of having their privacy violated.Social networks like Facebook on the Internet provide an overplus of knowledge concerning their users.Although users relish exchanging data online,only some data are meant to be interpreted by those who see value in it.It is now essential for online social network(OSN)to regulate the privacy of their users on the Internet.This paper aims to propose an efficient privacy violation detection model(EPVDM)for OSN.Design/methodology/approach-In recent months,the prominent position of both industry and academia has been dominated by privateness,its breaches and strategies to dodge privacy violations.Corporations around the world have become aware of the effects of violating privacy and its effect on them and other stakeholders.Once privacy violations are detected,they must be reported to those affected and it’s supposed to be mandatory to make them to take the next action.Although there are different approaches to detecting breaches of privacy,most strategies do not have a functioning tool that can show the values of its subject heading.An EPVDM for Facebook,based on a deep neural network,is proposed in this research paper.Findings-The main aim of EPVDM is to identify and avoid potential privacy breaches on Facebook in the future.Experimental analyses in comparison with major intrusion detection system(IDS)to detect privacy violation show that the proposed methodology is robust,precise and scalable.The chances of breaches or possibilities of privacy violations can be identified very accurately.Originality/value-All the resultant is compared with well popular methodologies like adaboost(AB),decision tree(DT),linear regression(LR),random forest(RF)and support vector machine(SVM).It’s been identified from the analysis that the proposed model outperformed the existing techniques in terms of accuracy(94%),precision(99.1%),recall(92.43%),f-score(95.43%)and violation detection rate(>98.5%).
    • Mohd Anul Haq; Mohd Abdul Rahim Khan; Talal AL-Harbi
    • 摘要: Intrusion Detection System(IDS)plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices.However,anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection.Also,we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks.Given this background,we designed a solution to detect intrusions using the Convolutional Neural Network(CNN)for Enhanced Data rates for GSM Evolution(EDGE)Computing.We created two separate categories to handle the attack and non-attack events in the system.The findings of this study indicate that this approach was significantly effective.We attempted both multiclass and binary classification.In the case of binary,we clustered all malicious traffic data in a single class.Also,we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset NSL-KDD.Principal Component Analysis(PCA)was implemented to decrease the size of the feature vector based on feature extraction and engineering.The approach proposed in the current investigation obtained accuracies of 99.34%and 99.13%for binary and multiclass classification,respectively,for the NSL-KDD dataset.The experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network(PCCNN)approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.
    • Tahani Alatawi; Ahamed Aljuhani
    • 摘要: The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable systems.Although the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big challenge.An anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT environments.In this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data source.The anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection performance.In the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or malicious.To validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack protocols.The experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.
    • Mahmoud Ragab; Ali Altalbe
    • 摘要: Due to the drastic increase in the number of critical infrastructures like nuclear plants,industrial control systems(ICS),transportation,it becomes highly vulnerable to several attacks.They become the major targets of cyberattacks due to the increase in number of interconnections with other networks.Several research works have focused on the design of intrusion detection systems(IDS)using machine learning(ML)and deep learning(DL)models.At the same time,Blockchain(BC)technology can be applied to improve the security level.In order to resolve the security issues that exist in the critical infrastructures and ICS,this study designs a novel BC with deep learning empowered cyber-attack detection(BDLE-CAD)in critical infrastructures and ICS.The proposed BDLE-CAD technique aims to identify the existence of intrusions in the network.In addition,the presented enhanced chimp optimization based feature selection(ECOA-FS)technique is applied for the selection of optimal subset of features.Moreover,the optimal deep neural network(DNN)with search and rescue(SAR)optimizer is applied for the detection and classification of intrusions.Furthermore,a BC enabled integrity checking scheme(BEICS)has been presented to defend against the misrouting attacks.The experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying aspects.The simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the accuy of 92.63%.
    • 张克柱
    • 摘要: 随着互联网技术应用的快速发展,人们对网络信息依赖程度越来越高,信息安全显得尤为重要,通过对常见的网络安全防护技术的分析与研究,提出基于LCS算法的网络攻击行为分析与防护技术,并搭建了蜜罐系统进行实验与测试,实现了对黑客攻击行为进行监控与分析的功能,使网络安全性能得到较大的提升。
    • 戴丹青; 孙丽; 杨志高
    • 摘要: 2022年9月5日四川省甘孜州泸定县发生M_(W)6.6地震,利用国家烈度速报与预警工程项目建成的基本站强震动数据,使用迭代反褶积和叠加法(IDS)进行破裂过程反演。反演所得破裂模型显示,破裂面呈NNW—SSE走向,破裂持续时间为15 s,分为4个阶段:首个阶段发生在震后3 s,破裂朝着断层面上倾方向以及SE侧传播;第二阶段为震后6—9 s,破裂继续向SE侧传播并在震中SE侧10 km处迅速加剧,此时破裂滑动速率达到峰值;第三阶段在震后9—12 s,破裂能量继续在SE侧释放,破裂滑动速率逐渐减小,破裂静态滑动累积量达到峰值并趋于稳定;第四阶段在震后12—15 s,破裂能量基本释放完毕,破裂结束。整个破裂由震中向SE方向延伸,由深部向浅部扩展。最大破裂点位于震中SE向10 km附近地下5 km处,最大滑动量为0.8 m,破裂可能出露地表。
    • 张周晶; 申玲钰
    • 摘要: 工业互联网发展的过程中,针对工业协议的指令级IDS需求正在迅速增长,IEC104作为国家基础设施通信的基础工业协议是当前网络中监测、审计的重点关注协议.针对该需求,将IEC104的解析分析、监测告警及日志输出以插件模式在已有开源框架suricata中设计、开发及实现,满足当前系统需求.
    • 张云
    • 摘要: 精益六西格玛(LSS)是精益生产和六西格玛管理的结合,通过整合吸收IDS和6两种模式的优点,达到最佳的管理效果.项目计划管理一直是管理学中不可缺少的环节.将LSS的理念融入到贴合实际的项目管理计划中,提高了企业管理力度,减少资源浪费,行之有效的管理提高了企业的利润,开拓了企业的发展空间.我国是实行计划经济的社会主义国家,计划管理在整个经济管理中居于主导地位.文中通过对LSS的分析研究,提出了将LSS的理论运用到企业项目计划管理中的一种管理模式,为企业提升了经济效益节约了成本.
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