Ref HAL: hal-00686027_v2
Abstract: In classical databases, query performance is casually achieved through physical data structures such as caches, indexes and materialized views. In this context, many cost models help select a "best set'' of such data structures. However, this selection task becomes more complex in the cloud. The criterion to optimize is indeed at least two-dimensional, with the monetary cost of using the cloud balancing query response time. Thus, we define in this paper new cost models that fit into the pay-as-you-go paradigm of cloud computing. These cost models help achieve a multi-criteria optimization of the view materialization vs. CPU power consumption problem, under budget constraints. Finally, we present experimental results that provide a first validation of our contribution and show that cloud view materialization is always desirable.

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Ref HAL: hal-00667416_v1
Abstract: Avec l'avènement de l'informatique dans les nuages (Cloud Compu- ting) comme un nouveau modèle de déploiement des systèmes informatiques, les entrepôts de données profitent de ce nouveau paradigme. Dans ce contexte, il devient nécessaire de bien protéger ces entrepôts de données des différents risques et dangers qui sont nés avec l'informatique dans les nuages. En conséquence nous proposons dans ce travail une façon de limiter ces risques à travers l'algorithme le partage de clés secret de Shamir et nous mettons cette contribution en pratique.

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Ref HAL: hal-00686001_v1
Abstract: Data integration is a critical problem in data warehousing and decision-support systems. Traditional data integration systems are very successful in integrating structured data, but structured data represent only a small subset of interesting data that could be warehoused by many enterprises. Current data integration systems also lack of self-managing capabilities. Therefore, we propose a data integration framework for integrating complex data actively. The purpose of our framework is twofold. Firstly, it integrates complex data using Web standards into an Active XML (AXML) repository. Secondly, beside warehousing logged events into event repository, it exploits active rules and framework events mining to self-manage, automate and activate different data integration tasks. Finally, we have implemented a prototype framework as a web application.

Ref HAL: hal-00667304_v1
Abstract: Avec l'avènement de l'informatique dans les nuages (Cloud Computing) comme un nouveau modèle de déploiement des systèmes informatiques, les entrepôts de données profitent de ce nouveau paradigme. Dans ce contexte, il devient nécessaire de bien protéger ces entrepôts de données des différents risques et dangers qui sont nés avec l'informatique dans les nuages. En conséquence nous proposons dans ce travail une façon de limiter ces risques à travers l'algorithme le partage de clés secret de Shamir et nous mettons cette contribution en pratique.

Comments: Poster

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Ref HAL: hal-00667301_v1
Comments: http://drops.dagstuhl.de/opus/volltexte/2011/3317/

Ref HAL: hal-00631499_v1
Ref Arxiv: 1110.2704
Abstract: The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperforms several well-known methods.

Ref HAL: hal-00631503_v1
Abstract: The dramatic proliferation of sophisticated cyber attacks, in conjunction with the ever growing use of Internet-based services and applications, is nowadays becoming a great concern in any organization. Among many efficient security solutions proposed in the literature to deal with this evolving threat, ensemble approaches, a particular family of data mining, have proven very successful in designing high performance intrusion detection systems (IDSs) resting on the mutual combination of multiple classifiers. However, the strength of ensemble systems depends heavily on the methods to generate and combine individual classifiers. In this thread, we propose a novel design method to generate a robust ensemble-based IDS. In our approach, individual classifiers are built using both the input feature space and additional features exploited from k-means clustering. In addition, the ensemble combination is calculated based on the classification ability of classifiers on different local data regions defined in form of k-means clustering. Experimental results prove that our solution is superior to several well-known methods.

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