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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Bouazi, Aziz
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Computer Science & IT Electrical & Electronics Engineering

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Intelligent intrusion detection through deep autoencoder and stacked long short-term memory Moukhafi, Mehdi; Tantaoui, Mouad; Chana, Idriss; Bouazi, Aziz
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2908-2917

Abstract

In the realm of network intrusion detection, the escalating complexity and diversity of cyber threats necessitate innovative approaches to enhance detection accuracy. This study introduces an integrated solution leveraging deep learning techniques for improved intrusion detection. The proposed framework consists on a deep autoencoder for feature extraction, and a stacked long short-term memory (LSTM) network ensemble for classification. The deep autoencoder compresses raw network data, extracting salient features and mitigating noise. Subsequently, the stacked LSTM ensemble captures intricate temporal dependencies, correcting anomaly detection precision. Experiments conducted on the UNSW-NB15 dataset, and a benchmark in intrusion detection validate the effectiveness of the approach. The solution achieves an accuracy of 90.59%, with precision, recall, and F1-Score metrics reaching 90.65, 90.59, and 90.57, respectively. Notably, the framework outperforms standalone models and demonstrates the advantage of synergizing deep autoencoder-driven feature extraction with the stacked LSTM ensemble. Furthermore, a binary classification experiment attains an accuracy of about 90.59%, surpassing the multiclass classification and affirming the model's potential for binary threat identification. Comparative analyses highlight the pivotal role of feature extraction, while experimentation illustrates the enhancement achieved by incorporating the synergistic deep autoencoder-Stacked LSTM approach.
Face recognition based on landmark and support vector machine Afifi, Hassan; Hsaini, Abdallah Marhraoui; Merras, Mostafa; Bouazi, Aziz; Chana, Idriss
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1289-1298

Abstract

Nowadays, the fast development of face recognition technologies used in fields such as security and video surveillance, gives us many theories and algorithms, a view of these algorithms provides us with an idea of their performance and limitations. In this paper, we will develop a new face recognition approach using the face estimation landmark algorithm to detect faces in real-time videos. Then, we use a pre-trained neural network to extract the 128 facial features of each face detected in the database images and register each vector of 128 values with the corresponding person’s name. Then, we form the linear support vector machine (SVM) classifier to recognize faces. Extensive experiments on real and generated data are presented to demonstrate the quality of the proposed method in terms of accuracy, reliability, and speed.
Co-Authors Afifi, Hassan Chana, Idriss Hsaini, Abdallah Marhraoui Merras, Mostafa Moukhafi, Mehdi Tantaoui, Mouad