International audience

Delegation is a very important part of the administrative process in access control systems; it provides resiliency and flexibility regarding to the management procedure. Delegation is the process of granting a specific authorization from a user to another user of the same system to carry out some functions on his behalf. The delegation, although widely used, is modeled in very little security policies because of its complexity. In this paper we aim to consider the delegation dynamically based on temporal context, to this end we redefine delegation for OrBAC using temporal nonmonotonic description logic. OrBAC is an access control model; it provides the mean to specify contextual authorizations, which facilitates modeling delegation features such as temporary delegation, multiple delegation, revocation, etc. The description logic that we use for the re-formalization process is T- JClassic δɛ . This logic gives the mean to specify nonmonotonic authorizations, and a better representation of the temporal aspects specific to a given delegation. This new representation augments the expressivity of the model and therefore it facilitates even more the representation and the management of the delegation characteristics.

While uncertainty can't be ignored in real-world problems, there is almost no research work addressing this issue in the recommender systems framework, especially all that relates to user ratings preferences. Indeed, the subjectivity of user's rating and his/her changing preferences over time, make them subject to uncertainty. Usually, user's imprecise rating for an item (product or service) is time-dependent information and generally provided much later. Meantime the item may change either by degrading or improving its inherent quality. The rating therefore may deviate, since it doesn't describe faithfully the actual current state of the item. This deviation leads to a form of uncertainty on user preferences that we handle in this paper. We show that uncertainty is an ubiquitous aspect in building recommender systems and its taking into account can help predicting the most accurate items by improving their certainty degrees.

The use of ontologies to represent geographical knowledge has received a lot of attention recently. Nevertheless, classical ontol- ogy languages are not able to represent incomplete and uncertain knowledge, which are very important characteristics of several situations in geographic domain. Based on our previous work, we propose a solution for handling uncertainty and for dealing with inconsistency in geographical applications. In this paper, we present an extension of the OWL 2, named Poss-OWL 2, based on our Poss - SROIQ(D) description logic (DL). Then we describe some reflections about the inference system. Illustrative examples, from archaeological domain, are given.

In ambient intelligence environments, the information provided by robot's embedded sensors and physical or logical entities may be inaccurate and uncertain. The Dempster-Shafer evidence Theory (DST) gives a mathematical convenient framework for the evidential fusion representation and inference of uncertain information. However, DST yields counterintuitive results in high conflicting ambient intelligence situations. This paper aims to provide a new strategy to manage conflict in activity recognition process in the ambient intelligence applications. It addresses the challenge of uncertainty and proposes an evidential fusion model based on the management of conflicting situation to optimize decision making in activity recognition. The proposed approach gives intuitive interpretation for combining multiple sources in conflicting situations and avoids the problems of using The Dempster-Shafer rule of combination.

OWLδϵis an ontological language for the semantic web extended by two operators “Default(C)” and “Exception(C)”. The aim of this extension is to bring out the typical aspect of concepts and their exceptions. This language is based on a non monotonic description logic named ExtDLδϵ.

This paper presents a new approach in the management of mobile ad hoc networks. Our alternative, based on mobile agent technology, allows the designing of mobile centralized servers in ad hoc network, where it is not obvious to think about a centralized management, due to the absence of any administration or fixed infrastructure in these networks. The object of this centralized approach is to provide permanent availability of services in ad hoc networks which are characterized by a distributed management. In order to evaluate the performances of the proposed approach, we apply it to solve the problem of mobile code localization in ad hoc networks. It is the matter to localize an object of double mobility, first, in relation to the mobility of its physical support in a dynamic environment, the station, and second, that of the code through the network stations.

Resources performance forecasting constitutes one of particularly significant research problems in distributed computing. To ensure an adequate use of the computing resources in a metacomputing environment, there is a need for effective and flexible forecasting method to determine the available performance on each resource. In this paper, we present a modeling approach to estimating the future value of CPU load. This modeling prediction approach uses the combination of adaptive network-based fuzzy inference systems (ANFIS) and the clustering process applied on the CPU Load time series. Experiments show the feasibility and effectiveness of this approach that achieves significant improvement and outperforms the existing CPU load prediction models reported in literature.

In the organization based access control (ORBAC) model, to express security policy, it is necessary to make possible the system know which are the privileges of each user. The definition of permission should not be static, but it must depend on the requirement of the system, rules should be dynamic, depending on the context. Context is used to specify the concrete circumstances where user is given role permissions to perform activities on views. Formalization of ORBAC in a logical approach makes it feasible to reason about a specified policy and verifies its correctness. We propose a formal modelisation of ORBAC by the description logic language with default and exception ALdeltaepsiv. We show how exception in information system security can be captured by ALdeltaepsiv. We illustrate this approach by an example of a medical information system.

With the development of communications and mobile networks, the localization mechanism is a major component of future services. In this paper, we study the localization problem of mobile agents in ad hoc mobile networks; an object which has a double mobility is to be located, first, in relation to the mobility of its physical support in a dynamic environment, the station, and that of the code through the network stations. A performances evaluation, based upon a simulation, of proposed localization protocols show that the centralized approach in a distributed form is more interesting than a totally centralized approach.

Alert correlation is a very useful mechanism to reduce the high volume of reported alerts and to detect complex and coordinated attacks. Existing approaches either require a large amount of expert knowledge or use simple similarity measures that prevent detecting complex attacks. They also suffer from high computational issues due, for instance, to a high number of possible scenarios. In this paper, we propose a Naive Bayes approach to alert correlation. Our modeling only needs a small part of expert knowledge. It takes advantage of available historical data, and provides efficient algorithms for detecting and predicting most plausible scenarios. Our approach is illustrated using the well known DARPA 2000 data set.