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3. Computing a Possibility Theory Repair for Partially Preordered Inconsistent Ontologies.

Author

Belabbes, Sihem and Benferhat, Salem

Subjects
DESCRIPTION logics, POLYNOMIAL time algorithms, POSSIBILITY, KNOWLEDGE base, REPAIRING
Abstract

We address the problem of handling inconsistency in uncertain knowledge bases that are specified in the lightweight fragments of description logics DL-Lite. More specifically, we assume that the TBox component is coherent, stable, and fully reliable. However, the ABox component may be inconsistent with respect to the TBox, partially preordered and uncertain. Uncertainty is encoded in the framework of possibility theory. In this context, we propose an extension of standard possibilistic DL-Lite. We represent the ABox as a symbolic weighted base, where the weights attached to the assertions are ordered according to a strict partial order. We define a tractable method for computing a single possibilistic repair for a partially preordered weighted ABox. The idea is to consider the possibilistic compatible bases of such an ABox, which intuitively encode all the possible extensions of a partial order, and compute the possibilistic repair of each compatible base. We then compute the intersection of all these possibilistic repairs to obtain a single repair for the initial ABox. We also provide an equivalent characterization by introducing the notion of $\pi$ -accepted assertions. This ensures that the computation of the partially preordered possibilistic repair can be achieved in polynomial time in DL-Lite. [ABSTRACT FROM AUTHOR]

Published
2022
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37. Revising the Outputs of a Decision Tree with Expert Knowledge: Application to Intrusion Detection and Alert Correlation.

Author

Benferhat, Salem, Boudjelida, Abdelhamid, and Tabia, Karim

Abstract

Classifiers are well-known and efficient techniques used to predict the class of items descrided by a set of features. In many applications, it is important to take into account some extra knowledge in addition to the one encoded by the classifier. For example, in spam filtering which can be seen as a classification problem, it can make sense for a user to require that the spam filter predicts less than a given rate or number of spams. In this paper, we propose an approach allowing to combine expert knowledge with the results of a decision tree classifier. More precisely, we propose to revise the outputs of a decision tree in order to take into account the available expert knowledge. Our approach can be applied for any classifier where a probability distribution over the set of classes (or decisions) can be estimated from the output of the classification step. In this work, we analyze the advantage of adding expert knowledge to decision tree classifiers in the context of intrusion detection and alert correlation. In particular, we study how additional expert knowledge such as "it is expected that 80% of traffic will be normal" can be integrated in classification tasks. Our aim is to revise classifiers' outputs in order to fit the expert knowledge. Experimental studies on intrusion detection and alert correlation problems show that our approach improves the performances on different benchmarks. [ABSTRACT FROM PUBLISHER]

Published
2012
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38. Encoding default-based SELinux-security policy in Organization-Based Access Control Model.

Author

Benferhat, Salem, Bouriche, Khalid, and Ouzarf, Mohamed

Abstract

SELinux is one of most used access control models in Linux operating systems. This paper provides an encoding of a default SELinux security policy using the Organization based access control model (Orbac). We will use Fedora 14 as an example of a Linux distribution in order to illustrate our encoding. For each concept (role, type, context,‥) used in SELinux we provide its counterpart in Orbac model. This confirms the expressive power of Orbac model [ABSTRACT FROM PUBLISHER]

Published
2011

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