Browsing by Author "Supervisor: Fernini, Belabdelouahab"

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    AachabOnt: an ontology for alternative medicine
    (University of M'sila, 2015-06-10) HARKAT, Zohra; Supervisor: BRAHIMI, Mahmoud; Supervisor: Fernini, Belabdelouahab
    In recent years, the field of alternative medicine (Phytotherapy) knew a big evolution and became popular for several reasons. In addition to its high therapeutic value, it does not cost much. We have developed an ontology containingthe most important information on medicinal plantsand the diseasesthey treat. The method used was METHONTOLOGY. It was implemented by the use of the Protégé programand evaluatecl by the useof FaCT++-inference engine. The AachabOnt ontology was saved in the RDF language. To allow users utilize the AachaOnt ontology, we have developed a website that allows research through the use of Jenatechnology and the SPARQL and JSP query languages.
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    Analyzing an XML document using mobile agents
    (University of M'sila, 2015-06-10) Boutchicha, Nossiba; Supervisor: Meliouh, Amel; Supervisor: Fernini, Belabdelouahab
    A mobile agent is a composition of computer software and data, which is able to migrate from one cornputer to another autonomously and continue its execution on the destination computer. For this reason, we use it to analyze an XML document. To perform this task, we have developed an application Chat allows users to extract information from an XML document using mobile agents.
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    An information theoretic approach to detect SQLI Intrusion
    (University of M'sila, 2015-06-10) BOUKAROUI, HADJER; Supervisor: SAOUDI, LALIA; Supervisor: Fernini, Belabdelouahab
    SQL Injection (SQLI) is a widespread vulnerability commonly found in web-based programs. Exploitations of SQL injection vulnerabilities lead to harmful consequences such as authentication bypassing and leakage of sensitive personal information. Therefore, SQLI needs to be mitigated to protect end users. In this work, we present an approach to detect SQLI attacks based on information theory. We compute the entropy of each query present in a program accessed before program deployment. During the program execution time, when an SQL query is invoked, we compute the entropy again to identify any change in the entropy measure for that query. The approach then relies on the assumption that dynamic queries with attack inputs result in increased or decreased level of entropy. In contrast, a dynamic query with benign inputs does not result in any change of entropy value.