Journal of Digital Information Management最新文献_第3页

Information Security Risk Management of Research Information Systems: A hybrid approach of Fuzzy FMEA, AHP, TOPSIS and Shannon Entropy 科研信息系统的信息安全风险管理:模糊FMEA、层次分析法、TOPSIS和Shannon熵的混合方法

Journal of Digital Information Management Pub Date : 2019-12-01 DOI: 10.6025/jdim/2019/17/6/321-336

M. Ershadi, Mehrdad Forouzandeh

引用次数: 4

Model Integration Approach from SysML to MATLAB/Simulink 从SysML到MATLAB/Simulink的模型集成方法

Journal of Digital Information Management Pub Date : 2018-12-01 DOI: 10.6025/jdim/2018/16/6/289-307

Bassim Chabibi, A. Anwar, M. Nassar

{"title":"Model Integration Approach from SysML to MATLAB/Simulink","authors":"Bassim Chabibi, A. Anwar, M. Nassar","doi":"10.6025/jdim/2018/16/6/289-307","DOIUrl":"https://doi.org/10.6025/jdim/2018/16/6/289-307","url":null,"abstract":"In system engineering process, descriptive system models seem to be insufficient in order to perform a system verification which fulfils various stakeholders’ requirements. This aspect is well handled by simulation process through the use of several simulation techniques or algorithms. As a consequence, design process efficiency is considerably reduced by the fact that both system modeling and simulation tools are often used separately. This study introduces an integration process to unify the potential provided by systems modeling languages and simulation environments, through the definition of a Domain Specific Language, namely Simulation Modeling Language, that is built on the basis ofa deep study of common constructs, semantics and modeling methodologies of several simulation environments, in addition to the specification of a model transformation between this language and a simulation environment (MATLAB/SImulink) in order to illustrate both its importance et its efficiency in our integration approach. Through the specification of its syntaxes and semantics, the defined intermediate modeling language allows modeling systems by using common constructs and modeling methodologies of simulation process in order to ensure their modeling with simulation environments and, thus, con duct experiences and system verifications. The definition of this language constitutes the basis of our integration approach aiming to bridge the gap between system modeling and simulation aspects in order to benefit from the strengths and potentials of both approaches. The integration approach consists on the specification of a bidirectional transformation, based on the concepts of ModelDriven Engineering, to perform in future works. Subject Categories and Descriptors I.6 [Simulation and Modeling] I.6.5 [Model Development]; Modeling methodologies ; F.3.2 [Semantics of Programming Languages] General Terms: Modeling language, Simulation, System Engineering, Model Driven Engineering","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114313831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Parking System Development Using Extreme Programming Method 用极限编程方法开发停车系统

Journal of Digital Information Management Pub Date : 2018-12-01 DOI: 10.6025/JDIM/2018/16/6/279-288

Johannes Fernandes Andry, Gary Juliawan, Y. Christian, J. Leonardo, Nicolas

引用次数: 4

Machine Learning in Predicting the Appropriate Model of Software Process-Models Deviation 机器学习在预测软件过程模型偏差中的应用

Journal of Digital Information Management Pub Date : 2018-12-01 DOI: 10.6025/JDIM/2018/16/6/308-323

Tarik Chaghrouchni, Issam Mohammed Kabbaj, Z. Bakkoury

引用次数: 0

A Novel Framework for Context-aware Outlier Detection in Big Data Streams 大数据流中上下文感知异常点检测的新框架

Journal of Digital Information Management Pub Date : 2018-10-01 DOI: 10.6025/JDIM/2018/16/5/213-222

Hussien Ahmad, S. Dowaji

{"title":"A Novel Framework for Context-aware Outlier Detection in Big Data Streams","authors":"Hussien Ahmad, S. Dowaji","doi":"10.6025/JDIM/2018/16/5/213-222","DOIUrl":"https://doi.org/10.6025/JDIM/2018/16/5/213-222","url":null,"abstract":"Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy. Journal of Digital Information Management Subject Categories and Descriptors H.2 [Database Management] H.2.8 Database Applications];","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114101593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Noise-Robust Image Encryption Algorithm Based on Hyper Chaotic Cellular Neural Network 一种基于超混沌细胞神经网络的抗噪声图像加密算法

Journal of Digital Information Management Pub Date : 2018-10-01 DOI: 10.6025/JDIM/2018/16/5/230-245

Gangyi Hu, Sumeth Yuenyong, Jian Qu, Jiang Rong, W. Kou

引用次数: 2

A Log Based Update of Replicated Profiles in Decentralized Social Networks 分散式社交网络中基于日志的复制配置文件更新

Journal of Digital Information Management Pub Date : 2018-10-01 DOI: 10.6025/JDIM/2018/16/5/223-229

Lyes Badis, M. Amad, D. Aïssani

引用次数: 1

New Approach for Intrusion Detection in Big Data as a Service in the Cloud 云中的大数据即服务入侵检测新方法

Journal of Digital Information Management Pub Date : 2018-10-01 DOI: 10.6025/JDIM/2018/16/5/246-257

Dounya Kassimi, O. Kazar, O. Boussaid, Abdelhak Merizig

引用次数: 2

Optimal Web Service Selection Model using Fuzzy Extended AHP and Weighted Sum Method 基于模糊扩展AHP和加权和方法的最优Web服务选择模型

Journal of Digital Information Management Pub Date : 2018-08-01 DOI: 10.6025/JDIM/2018/16/4/157-168

R. Kohar

引用次数: 3

Cloud Computing Intrusion Detection Using Artificial Bee Colony-BP Network Algorithm 基于人工蜂群bp网络算法的云计算入侵检测

Journal of Digital Information Management Pub Date : 2018-08-01 DOI: 10.6025/jdim/2018/16/4/203-209

Yang Hui

{"title":"Cloud Computing Intrusion Detection Using Artificial Bee Colony-BP Network Algorithm","authors":"Yang Hui","doi":"10.6025/jdim/2018/16/4/203-209","DOIUrl":"https://doi.org/10.6025/jdim/2018/16/4/203-209","url":null,"abstract":"Cloud computing characterizes a methodology for computing communications in a much effective manner, and a business paradigm for trading computing resources and services. Alternatively, these difficult and distributed planning’s turn a striking objective for intruders. Cloud computing provides huge latent for enhancing production and decrease expenditures. However it simultaneously acquires several novel security risks. Intrusion Detection Systems (IDS) have been employed broadly for identifying malicious actions in network communication and hosts. In this work, an artificial bee colony-BP neural network algorithm is applied to the detection module, in order to detect the complicated aggressive behaviors. Through example verification, the artificial bee colony-BP network algorithm has improved intrusion detection efficiency and classification precision, and can effectively guarantee the safety of the cloud computing environment. Subject Categories and Descriptors [D.4.6 Security and Protection] [C.2 Cmputer Communication Networks] Security and protection [F.1.1 Models of Computation]; Neural networks General Terms: Cloud Computing, Neural Networks, IDS","PeriodicalId":197165,"journal":{"name":"Journal of Digital Information Management","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117244698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0