Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies
Abdelkader, Ghazli
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Securing DNS over HTTPS traffic: a real-time analysis tool Dhiya Eddine, Abid; Abdelkader, Ghazli
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p227-234

Abstract

DNS over HTTPS (DoH) is a developing protocol that uses encryption to secure domain name system (DNS) queries within hypertext transfer protocol secure (HTTPS) connections, thereby improving privacy and security while browsing the web. This study involved the development of a live tool that captures and analyzes DoH traffic in order to classify it as either benign or malicious. We employed machine learning (ML) algorithms such as K-Nearest Neighbors (K-NN), random forest (RF), decision tree (DT), deep neural network (DNN), and support vector machine (SVM) to categorize the data. All of the algorithms, namely KNN, RF, and DT, achieved exceptional performance, with F1 scores of 1.0 or above for both precision and recall. The SVM and DNN both achieved exceptionally high scores, with only slight differences in accuracy. This tool employs a voting mechanism to arrive at a definitive classification decision. By integrating with the Mallory tool, it becomes possible to locally resolve DNS, which in turn allows for more accurate simulation of DoH queries. The evaluation results clearly indicate outstanding performance, confirming the tool's effectiveness in analyzing DoH traffic for network security and threat detection purposes.
Enhancing malware detection with genetic algorithms and generative adversarial networks Eddine, Abid Dhiya; Abdelkader, Ghazli; Mourad, Bouache
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3064-3074

Abstract

Malware detection is a critical task in cybersecurity, necessitating the creation of robust and accurate detection models. Our proposal employs a holistic methodology for identifying and mitigating malware using deep learning techniques. Initially, a customized genetic algorithm is employed for feature selection, reducing dimensionality and enhancing the discriminatory power of the dataset. Subsequently, a deep neural network is trained on the selected features, achieving high accuracy and robust performance in distinguishing between malware and benign data. Generative adversarial networks are also utilized to evaluate model effectiveness on unseen data and ensure the model's robustness and generalization capabilities. Evaluation of the proposed model demonstrates accurate malware detection with high generalization capabilities. Furthermore, future research should focus on developing and deploying practical tools or systems that implement the proposed model for real-time malware detection in operational environments. This research makes a significant contribution to the field of malware detection and provides excellent opportunities for practical implementation in the field of cybersecurity.
Smartphone-based fingerprint authentication using siamese neural networks with ridge flow attention mechanism Imane, Benchergui Malika; Abdelkader, Ghazli; Benaoumeur, Senouci M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1622-1632

Abstract

Authenticating finger photo images captured using a smartphone camera provides a good alternative solution in place of the traditional method-based sensors. This paper introduces a novel approach to enhancing fingerprint authentication by leveraging images captured via a mobile camera. The method employs a siamese neural network (SNN) combined with a ridge flow attention mechanism and convolutional neural networks (CNN). Our approach begins with collecting a dataset consisting of finger images from two individuals then we apply multiple preprocessing techniques to extract fingerprint images, followed by generating augmented data to improve model robustness, scaling, and normalizing them to form images suitable for model training. Next, we generate positive and negative pairs for training a SNN. We used the SNN with CNN for feature extraction, combined with an attention mechanism that focuses on the ridge flow pattern of fingerprints to improve feature relevance which significantly contributed to the performance enhancement. As for the testing performance, our model has an accuracy of 90%, precision of 89%, recall of 83%, F1 score of 86%, area under the curve (AUC) 95 %, and 13% of equal error rate (EER) when using smartphone-captured images for fingerprint recognition.
Co-Authors Benaoumeur, Senouci M. Dhiya Eddine, Abid Eddine, Abid Dhiya Imane, Benchergui Malika Mourad, Bouache