Latifa Hamami-Mitiche

Latifa Hamami-Mitiche

مطالب
ترتیب بر اساس: جدیدترینپربازدیدترین

فیلترهای جستجو: فیلتری انتخاب نشده است.
نمایش ۱ تا ۲ مورد از کل ۲ مورد.
۱.

AI-Enhanced Intrusion Detection: Integrating Expert Knowledge and Machine Learning for Enterprise Networks(مقاله علمی وزارت علوم)

کلیدواژه‌ها: Intrusion Detection Systems Artificial Intelligence deep learning Machine Learning

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تعداد بازدید : ۱۰ تعداد دانلود : ۱۸
Enterprise networks, as the backbone of modern information systems, are increasingly exposed to sophisticated and rapidly evolving cyber threats. Traditional Intrusion Detection Systems (IDS), based on static attack signatures, often fail to detect novel or complex intrusions, resulting in high false alarm rates. This study proposes an intelligent IDS that leverages Machine Learning and Deep Learning techniques to significantly improve detection accuracy and reduce alert noise. The system is capable of classifying attacks by severity and provides an intuitive interface to support efficient threat monitoring. Beyond technical performance, the solution addresses managerial objectives by lowering maintenance costs, enhancing service quality, accelerating incident response, and ensuring high availability with straightforward deployment. The proposed model offers a scalable and resilient IDS tailored for enterprise environments, contributing both practical and strategic value in the fight against increasingly sophisticated cyberattacks.
۲.

Implementation of Face Recognition Algorithm on Fields Programmable Gate Array Card(مقاله علمی وزارت علوم)

کلیدواژه‌ها: Fields programmable gate array VHSIC Hardware description language Principal Component Analysis Manhattan Distance

حوزه‌های تخصصی:
تعداد بازدید : ۴۷۲ تعداد دانلود : ۲۲۰
The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition, then use a VHSIC Hardware Description Language (VHDL) simulation and design platform, which is the ISE. We describe the operation of each block and implement, thereafter, the computation of the global centered images. This corresponds to the first step of the PCA algorithm to assess its performance. The comparison of the results of this implementation with that of MATLAB confirmed the operability and effectiveness of this method for centralizing images. We also implemented the last part of this algorithm which is the computation of the Manhattan distance. The tests have given very satisfactory results.

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