@article {793, title = {An Agent-Based Approach for Extracting Business Association Rules from Centralized Databases Systems-}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {10/2019}, pages = {270-288}, type = {Research}, chapter = {270}, abstract = {

Today, enterprises use a variety of applications to manage day-by-day business activities using a large centralized database. Since a huge amount of data stored in this centralized database produced by the daily use of several systems, it is important to integrate decision-making tools to analyse and interpret these business data. For this purpose, Data Mining is a powerful technology that promote information and knowledge extraction from large databases. In this paper, we present an agent-based approach for extracting business association rules from centralized database systems. This approach combine paradigm of multi-agent system and the association rules as a data mining technique to build anefficient model. It is relying on the intelligent partitioning of data to make the execution of business association rules in a parallel and distributed way from a large centralized database. To validate our approach, we applied it during the realization of a real case study on ERP database at the National company of Well Services (ENSP)using JADE platform with machine learning WEKA toolbox for association rules mining. The developed system has been compared with the classic association rules algorithms and has proved it is more efficient and more scalable. The main objective of our work is to improve and accelerate the process of extracting association rules by business through centralized database systems. As a result, the decision process of these systems becomes more improved.

}, doi = {https://doi.org/10.6025/jdim/2019/17/5/270-288}, url = {http://dline.info/fpaper/jdim/v17i5/jdimv17i5_2.pdf}, author = {Nadjib Mesbahi and Merouane Zoubeidi and Abdelhak Merizig and Okba Kazar} } @article {716, title = {Computer and Network Security: Ontological and Multi-agent System for Intrusion Detection}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {06/2019}, pages = {133-144}, type = {Research}, chapter = {133}, doi = {https://doi.org/10.6025/jdim/2019/17/3/133-144}, url = {http://dline.info/fpaper/jdim/v17i3/jdimv17i3_3.pdf}, author = {Samir Bourekkache and Okba Kazar and Ahmed Aloui} } @article {801, title = {Computer and Network Security: Ontological and Multi-agent System for Intrusion Detection-}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {06/2019}, pages = {133-144}, type = {Research}, chapter = {133}, abstract = {

Today the security aspect is a backbone of every computer system, particularly when this system contains sensitive information. When we use our computers and connect to the network or use web services, we trust that our personal and sensitive information (images, passwords, credit card number {\textellipsis}etc) are confidential and well secured by the used security system. Unfortunately, the hackers may succeed to gain access to the system and misuse our information. Generally, the aim of the intrusion is to find new ways to annoy, steal and harm parts of computer systems. Moreover, with the possibility of connecting several computers and networks, the necessity of protecting the whole data and machines from attackers (hackers) that try to get some confident information to use for their own benefit or just destroy or modify valuable information was born. At this point IDS appears to help users, companies or institutions to detect when they are getting compromised. In this paper, we develop an intrusion detection system using multi agent system, ontological techniques (IDSMAO) and misuse method.

}, doi = {https://doi.org/10.6025/jdim/2019/17/3/133-144}, url = {http://dline.info/fpaper/jdim/v17i3/jdimv17i3_3.pdf}, author = {Samir Bourekkache and Okba Kazar and Ahmed Aloui} } @article {705, title = {Web Service Selection Approach Based on Agent and Fuzzy Logic}, journal = {Journal of Digital Information Management}, volume = {19}, year = {2019}, month = {02/2019}, type = {Research}, chapter = {1-13}, doi = {https://doi.org/10.6025/jdim/2019/1/1-12}, author = {Houcine Belouaar and Okba Kazar and Nadia Kabachi} } @article {808, title = {Web Service Selection Approach Based on Agent and Fuzzy Logic-}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {02/2019}, pages = {1-12}, type = {Research}, chapter = {1}, abstract = {

The context of this work is the selection of the services among a list of similar services. To differentiate between similar web services, the best approach to use is the one based on service quality (QoS) criteria. However, these criteria most of the time are imprecise and sometimes uncertain and ambiguous because they are based on discrete numerical data. In some situation, we need to apply the concept of partial truth, where the partial truth values are between two numerical values. The fuzzy logic can be applied to support the representation of imprecise, uncertain and ambiguous representation. It is based on the observation that people make decisions based on imprecise and nonnumerical information. In this work, we propose an approach for selecting web services based on the fuzzy logic. Indeed, we use fuzzy logic by translating the QoS values that are attributed to services into linguistic terms. In addition, we also allow the service client to formulate its requests in the form of linguistic terms. In this process, the system selects and render the desired web service to the client. Our solution is represented as three layers, i) User Layer, that receives functional or fuzzy requests from clients which treats and forward them to the next layer, ii) Basic Layer that implements the service-oriented architecture and also, obtains the QoS values from service repository; and iii) Fuzzy Layer that takes as input the quality criteria of each web service then proceeds to their fuzzification, ranking and finally assign the scores to the different web services. To validate our solution, we implemented it as a multi-agent system.

}, doi = {https://doi.org/10.6025/jdim/2019/17/1/1-12}, url = {http://dline.info/fpaper/jdim/v17i1/jdimv17i1_1.pdf}, author = {Houcine Belouaar and Okba Kazar and Nadia Kabachi} } @article {820, title = {New approach for intrusion detection in Big Data as a service in the Cloud-}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {10/12}, pages = {258-270}, type = {Research}, chapter = {258}, abstract = {

\ Nowadays, Big Data has reached every area of our lives because it covers many tasks in different operation. This new technique forces the cloud computing to use it as a layer, for this reason cloud technology embraces it as Big Data as a service (BDAAS). After solving the problem of storing huge volumes of information circulating on the Internet, remains to us how we can protect and ensure that this information are stored\  without loss or distortion. The aim of this paper is to study the problem of safety in BDAAS, inparticularly we will cover the problem of Intrusion detection system (IDS). In order to solve the problems tackled in this paper, we have proposed a Self-Learning Autonomous Intrusion Detection system (SLA-IDS) which is based on the architecture ofautonomic system to detect the anomaly data. In this approach, to add the autonomy aspect to the proposed system we have used mobile and situated agents. The implementation of this model has been provided to evaluate our system. The obtained findings show the effectiveness of our proposed model. We validate our proposition using Hadoop as Big Data Platform and CloudSim, machine learning Weka with java to create Model of detection.

}, doi = {https://doi.org/10.6025/jdim/2018/16/5/258-270}, url = {http://dline.info/fpaper/jdim/v16i5/jdimv16i5_5.pdf}, author = {Dounya Kassimi and Okba Kazar and Omar Boussaid and Abdelhak Merizig} } @article {700, title = {New approach for intrusion detection in Big Data as a service in the Cloud}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {10/2018}, pages = {258-271}, type = {Research}, chapter = {258}, doi = {https://doi.org/10.6025/jdim/2018/5/258-270}, author = {Dounya Kassimi and Okba Kazar and Omar Boussaid and Abdelhak Merizig} }