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Item Installation et configuration d’une solution de détection d’intrusions sans fil (Kismet)(CERIST, 2011-06) Lounis, Karim; Babakhouya, Abdelaziz; Nouali-Taboudjemat, NadiaLes réseaux sans fil (Wi-Fi, norme 802.11) offrent plusieurs avantages ; notamment en terme de mobilité, coût, débit et facilité de déploiement. Cependant, ils sont par nature plus sensibles aux problèmes de sécurité. Les mécanismes de sécurité (WEP, WPA, WPA2) ne permettent pas de se prévenir contre tous les problèmes de sécurité. En effet, les bornes Wi-Fi restent toujours vulnérables aux intrusions : attaque de déni de service, rogue AP, attaque de dé-authentification. Dans cette optique, il est conseillé de surveiller régulièrement l'activité du point d'accès sans fil afin de détecter les activités anormales sur le réseau et de les signaler sous forme d’ALERTES. Ce rapport présent les détails techniques d’installation et de configuration d’une solution de détection d’intrusion pour un réseau Wi-Fi. Cette solution se base sur des logiciels open source (Linux, Kismet, OPENWRT, DD-WRT, SWATCH) et une série de matériels sans fil (routeur Linksys WRT54GL et carte réseau sans fil).Item Locating Emergency Responders using Mobile Wireless Sensor Networks(CERIST, 2012-07) Benkhelifa, Imane; Nouali-Taboudjemat, NadiaL'intervention d'urgence dans la gestion des catastrophes en utilisant les réseaux de capteurs sans fils est devenue récemment un intérêt de nombreux chercheurs du monde entier. Cet intérêt provient du nombre croissant de catastrophes et de crises (naturelles ou humaines) qui touchent des millions de vies ainsi que l'utilisation facile des nouvelles technologies pas cher. Ce document arrive avec un algorithme pour localiser les intervenants d'urgence et les secouristes en utilisant capteurs attachés aux secouristes. La solution proposée est très efficace et rapidement déployable et ne nécessite aucune infrastructure préinstallé. La solution est basée sur la prédiction des déplacements des secouristes en se basant sur des estimations de position précédentes. L'évaluation de notre solution montre que notre technique prend avantage de la prédiction d'une manière plus efficace que les solutions précédentes.Item Infrastructure de communication sans fil avec qualité de service pour la gestion de crises et catastrophes(CERIST, 2016-11) Kabou, Abdelbasset; Nouali-Taboudjemat, Nadia; Nouali, OmarLa Qualité de service (Quality of Service ou QoS) est un terme largement utilisé dans le domaine des technologies de communication. Dans les recommandations E.800, le CCITT (United Nations Consultative Committee for International Telephony and Telegraphy) défini la qualité de service comme : “Ensemble des effets portant sur les performances d’un service de communication et qui détermine le degré de satisfaction d’un utilisateur de ce même service”. Un intérêt particulier est porté dans ce rapport à la problématique du maintien de la QoS pour les applications multimédia. Ce type d'applications est très sensible aux variations des conditions régissant le réseau. Des métriques comme la bande passante, le taux de perte des paquets, la latence, la gigue ou des mécanismes tels le contrôle d’admission, les protocoles de signalisation, etc., sont de très grande importance pour ces applications et plus particulièrement durant une situation d'urgence. Ce rapport donne un aperçu de nos travaux sur la QoS des réseaux déployés en situation d'urgence.Item Multi and Many-core Parallel B&B approaches for the Blocking Job Shop Scheduling Problem(2016-07) Dabah, Adel; Bendjoudi, Ahcène; Ait Zai, Abdelhakim; El Baz, Didier; Nouali-Taboudjemat, NadiaIn this paper, we propose three approaches to accelerate the B&B execution time using Multi and Many-core systems to solve the NP-hard Blocking Job Shop Scheduling problem (BJSS). The first approach is based on Master/Worker paradigm where the workers independently explore the branches sent by the master. The second approach is a node-based parallelization that does not change the design of the B&B algorithm, except that the bounding process is faster since it is calculated in parallel using several threads organized in one GPU block. The third approach is a Multi-Core CPU/GPU hybridization that benefits from the power of both the CPU-cores and the GPU at the same time. This hybridization is based on concurrent kernels execution provided by Nvidia Multi process Service (MPS) i.e. each host process (Master or Worker) launches his own kernel to accelerate the bounding process on GPU. The obtained results using Taillard instances confirm the efficiency of our proposals. The first two approaches are respectively three and eighteen times faster compared to the sequential version. The results of the hybrid approach show a relative speedup over ninety times as compared to the sequential approach and therefore prove the advantage of using both the CPU-cores and the GPU at the same time.Item Data reordering for minimizing threads divergence in GPU-based evaluating association rules(CERIST, 2015-03-26) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Habbas, Zineb; Nouali-Taboudjemat, NadiaThis last decade, the success of Graphics Processor Units (GPUs) has led researchers to launch a lot of works on solving large complex problems by using these cheap and powerful architecture. Association Rules Mining (ARM) is one of these hard problems requiring a lot of computational resources. Due to the exponential increase of data bases size, existing algorithms for ARM problem become more and more inefficient. Thus, research has been focusing on parallelizing these algorithms. Recently, GPUs are starting to be used to this task. However, their major drawback is the threads divergence problem. To deal with this issue, we propose in this paper an intelligent strategy called Transactions- based Reordering "TR" allowing an efficient evaluation of association rules on GPU by minimizing threads divergence. This strategy is based on data base re-organization. To validate our proposition, theoretical and experimental studies have been carried out using well-known synthetic data sets. The results are very promising in terms of minimizing the number of threads divergence.Item Data reordering for minimizing threads divergence in GPU-based evaluating association rules(2015-06) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Habbas, Zineb; Nouali-Taboudjemat, NadiaThis last decade, the success of Graphics Processor Units (GPUs) has led researchers to launch a lot of works on solving large complex problems by using these cheap and powerful architecture. Association Rules Mining (ARM) is one of these hard problems requiring a lot of computational resources. Due to the exponential increase of data bases size, existing algorithms for ARM problem become more and more inefficient.Thus, research has been focusing on parallelizing these algorithms. Recently, GPUs are starting to be used to this task. However, their major drawback is the threads divergence problem. To deal with this issue, we propose in this paper an intelligent strategy called transactions-based Reordering ”TR” allowing an efficient evaluation of association rules on GPU by minimizing threads divergence. This strategy is based on data base re-organization. To validate our proposition, theoretical and experimental studies have been carried out using well-known synthetic datasets. The results are very promising in terms of minimizing the number of threads divergence.Item GPU-based Bees Swarm Optimization for Association Rules Mining(Springer, 2014) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, ZinebAssociation Rules Mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large scale data sets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as GPUs that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (SE-GPU and ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our approaches, the two algorithms have been executed to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our approaches improve the execution time up to x100 over the sequential mono-core BSO-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1 to 8 cores). The results show that they are faster than the multi-core versions what ever the number of used cores.Item Association rules mining using evolutionary algorithms (CERIST, 2014-10-16) Djenouri, Youcef; Bendjoudi, Ahcène; Nouali-Taboudjemat, NadiaThis paper deals with association rules mining using evolutionary algorithms. All previous bio-inspired based association rules mining approaches generate non admissible rules which the end-user can not exploit them. In this paper, we propose two approaches permit to avoid non admissible rules by developing new strategy called delete and decomposition strategy. If an item is appeared in the antecedent and the consequent parts of given rule, this rule is composed on two admissible rules. Then, we delete such item to the antecedent part of the first rule and we delete the same item to the consequent part of the second rule. We also proposed two approaches (IARMGA and IARMMA), the first approach uses a classical genetic algorithm in the search process. However, the second one employs a mimetic algorithm to improve the quality of returned rules. To demonstrate the suggested approaches, several experiments have been carried out using both synthetic and reals instances. The results reveal that it has a compromise between the execution time and the quality of output rules. Indeed, IARMGA is faster than IARMMA whereas the last one outperforms IARMGA in terms of rules quality.Item Association rules mining using evolutionary algorithms(LNCS, 2014-10-16) Djenouri, Youcef; Bendjoudi, Ahcène; Nouali-Taboudjemat, NadiaThis paper deals with association rules mining using evolutionary algorithms. All previous bio-inspired based association rules mining approaches generate non admissible rules which the end-user can not exploit them. In this paper, we propose two approaches permit to avoid non admissible rules by developing new strategy called delete and decomposition strategy. If an item is appeared in the antecedent and the consequent parts of given rule, this rule is composed on two admissible rules. Then, we delete such item to the antecedent part of the first rule and we delete the same item to the consequent part of the second rule. We also proposed two approaches (IARMGA and IARMMA), the first approach uses a classical genetic algorithm in the search process. However, the second one employs a mimetic algorithm to improve the quality of returned rules. To demonstrate the suggested approaches, several experiments have been carried out using both synthetic and reals instances. The results reveal that it has a compromise between the execution time and the quality of output rules. Indeed, IARMGA is faster than IARMMA whereas the last one outperforms IARMGA in terms of rules quality.Item An Efficient Measure for Evaluating Association Rules(CERIST, 2014-06-24) Djenouri, Youcef; Gheraibai, Youcef; Mehdi, Malika; Bendjoudi, Ahcène; Nouali-Taboudjemat, NadiaAssociation rules mining (ARM) has attracted a lot of attention in the last decade. It aims to extract a set of relevant rules from a given database. In order to evaluate the quality of the resulting rules, existing measures, such as support and confidence, allow to evaluate the resulted rules of ARM process separately, missing the different dependencies between the rules. This paper addresses the problem of evaluating rules by taking into account two aspects : (1) The accuracy of the returned rules on the input data and (2) the distance between the returned rules. The rules set that covers the maximum of rules space is considered. To analyze the behavior of the proposed measure, it has been tested on two recent ARM algorithms BSO-ARM and HBSO-TS.