02063nas a2200169 4500008004100000245010600041210006900147260001200216300001200228490000700240520150900247100002001756700002301776700002201799700001601821856005601837 2019 eng d00aAn Agent-Based Approach for Extracting Business Association Rules from Centralized Databases Systems-0 aAgentBased Approach for Extracting Business Association Rules fr c10/2019 a270-2880 v173 a

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.

1 aMesbahi, Nadjib1 aZoubeidi, Merouane1 aMerizig, Abdelhak1 aKazar, Okba uhttp://dline.info/fpaper/jdim/v17i5/jdimv17i5_2.pdf00493nas a2200145 4500008004100000245009400041210006900135260001200204300001200216490000700228100002300235700001600258700001700274856005600291 2019 eng d00aComputer and Network Security: Ontological and Multi-agent System for Intrusion Detection0 aComputer and Network Security Ontological and Multiagent System  c06/2019 a133-1440 v171 aBourekkache, Samir1 aKazar, Okba1 aAloui, Ahmed uhttp://dline.info/fpaper/jdim/v17i3/jdimv17i3_3.pdf01620nas a2200157 4500008004100000245009500041210006900136260001200205300001200217490000700229520111400236100002301350700001601373700001701389856005601406 2019 eng d00aComputer and Network Security: Ontological and Multi-agent System for Intrusion Detection-0 aComputer and Network Security Ontological and Multiagent System  c06/2019 a133-1440 v173 a

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 …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.

1 aBourekkache, Samir1 aKazar, Okba1 aAloui, Ahmed uhttp://dline.info/fpaper/jdim/v17i3/jdimv17i3_3.pdf00420nas a2200133 4500008004100000245006600041210006600107260001200173490000700185100002200192700001600214700001900230856003700249 2019 eng d00aWeb Service Selection Approach Based on Agent and Fuzzy Logic0 aWeb Service Selection Approach Based on Agent and Fuzzy Logic c02/20190 v191 aBelouaar, Houcine1 aKazar, Okba1 aKabachi, Nadia uhttps://dline.info/newl/node/70502139nas a2200157 4500008004100000245006700041210006600108260001200174300000900186490000700195520166600202100002201868700001601890700001901906856005601925 2019 eng d00aWeb Service Selection Approach Based on Agent and Fuzzy Logic-0 aWeb Service Selection Approach Based on Agent and Fuzzy Logic c02/2019 a1-120 v173 a

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.

1 aBelouaar, Houcine1 aKazar, Okba1 aKabachi, Nadia uhttp://dline.info/fpaper/jdim/v17i1/jdimv17i1_1.pdf01752nas a2200169 4500008004100000245008000041210006900121260001000190300001200200490000700212520123000219100002001449700001601469700001901485700002201504856005601526 2018 eng d00aNew approach for intrusion detection in Big Data as a service in the Cloud-0 aNew approach for intrusion detection in Big Data as a service in c10/12 a258-2700 v163 a

 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.

1 aKassimi, Dounya1 aKazar, Okba1 aBoussaid, Omar1 aMerizig, Abdelhak uhttp://dline.info/fpaper/jdim/v16i5/jdimv16i5_5.pdf00492nas a2200157 4500008004100000245007900041210006900120260001200189300001200201490000700213100002000220700001600240700001900256700002200275856003700297 2018 eng d00aNew approach for intrusion detection in Big Data as a service in the Cloud0 aNew approach for intrusion detection in Big Data as a service in c10/2018 a258-2710 v161 aKassimi, Dounya1 aKazar, Okba1 aBoussaid, Omar1 aMerizig, Abdelhak uhttps://dline.info/newl/node/700