Browsing by Author "Supervisor: SAOUDI, LALIA"

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    Mobile malware detection
    (UNIVERSITY of M'SILA, 2022-06-10) SOUFI, ABOUBAKER SEDDIK; BOUAZIZ, AZEDDINE; Supervisor: SAOUDI, LALIA
    With the advent of communication technologies and the extended availability of smart devices (smartphones, PDAs), mobiles applications are becoming part of our everyday life, due to its portability, availability and high performance services to cover most user’s needs. Like all web applications, mobile system is exposed to malware attacks. In this context, our project is aimed at developing a mobile malware detector (MobMal detector) on Android platform using machine learning approach to detect malware by mining the patterns of Permissions and API Calls.
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    ItemOpen Access
    Optimizéd XSS Vulnerability Scanner Approach
    (University of M'sila, 2016-06-10) BOULANOUAR, SOUHIL LARBI; Supervisor: SAOUDI, LALIA
    The Web applications are becoming more popular with the advancement of technology. However, the web security is becoming one of the most common security issues. This report focuses on the XSS vulnerabilities which commonly present in most Web applications and can create serious security problems. In our work, we propose a black box detection approach using optimal attack vector. This method generates an attack vector automatically, optimizes the attack vector repertory using a mutation operator model, and detects XSS vulnerabilities in web applications dynamically.