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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Ali Choukri
Ibn Tofail University
Computer Science & IT Engineering

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A transfer hybrid deep learning approach for advanced intrusion detection in IoT-based smart home security Mouad Choukhairi; Ouail Choukhairi; Youssef Fakhri; Ali Choukri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2750-2760

Abstract

As smart home environments increasingly rely on interconnected internet of things (IoT) devices, they face growing cyber threats originating both externally from malicious actors and internally from compromised or malfunctioning IoT devices. These threats, including unauthorized access, distributed denial of service (DDoS) attacks, and data exfiltration, pose significant risks to the security and privacy of smart home inhabitants. This paper introduces an advanced intrusion detection system (IDS) specifically designed to enhance the security of IoT-based smart home networks. Leveraging a hybrid deep learning approach combining convolutional neural networks (CNN) and long short-term memory (LSTM) models, complemented by transfer learning (TL) and hyper-parameter optimization techniques, our proposed IDS efficiently identifies both external and intra-network threats. Using the IoTID20 dataset, which simulates realistic attack scenarios, the IDS was trained and evaluated to detect abnormal behavior effectively within smart home networks. CNN layers extract spatial features from network traffic, while LSTM layers capture temporal dependencies, enabling robust detection against a range of cyber-threats. Evaluation results demonstrate the IDS’s high detection accuracy and exceptional F1-scores, validating its effectiveness in safeguarding IoT-based smart homes from evolving threats.
Overview of the progression of state-of-the-art language models Asmae Briouya; Hasnae Briouya; Ali Choukri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25936

Abstract

This review provides a concise overview of key transformer-based language models, including bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 3 (GPT-3), robustly optimized BERT pretraining approach (RoBERTa), a lite BERT (ALBERT), text-to-text transfer transformer (T5), generative pre-trained transformer 4 (GPT-4), and extra large neural network (XLNet). These models have significantly advanced natural language processing (NLP) capabilities, each bringing unique contributions to the field. We delve into BERT’s bidirectional context understanding, GPT-3’s versatility with 175 billion parameters, and RoBERTa’s optimization of BERT. ALBERT emphasizes model efficiency, T5 introduces a text-to-text framework, and GPT-4, with 170 trillion parameters, excels in multimodal tasks. Safety considerations are highlighted, especially in GPT-4. Additionally, XL-Net’s permutation-based training achieves bidirectional context understanding. The motivations, advancements, and challenges of these models are explored, offering insights into the evolving landscape of large-scale language models.
Exploration of image and 3D data segmentation methods: an exhaustive survey Hasnae Briouya; Asmae Briouya; Ali Choukri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25740

Abstract

The field of image and 3-dimensional (3D) data segmentation is growing fast and has many uses, like in medicine, and robotics. In this article, we explain how computers understand and divide images and 3D data. We compare different ways of doing this in 2D and 3D, and look at the computer methods used. We also discuss recent work and what they discovered. This article gives a broad overview of what’s happening in this area of computer science. It explains the goals of the research, how they do it, and what they’ve found out. It’s a useful guide for researchers to understand what’s happening now and what challenges they might face in the future.
Explainable deep learning for scalable record linkage: a TabNet-based framework for structured data integration Fatima Zahrae Saber; Ali Choukri; Mohamed Amnai; Abderrahim Waga
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp725-743

Abstract

Record linkage is considered a fundamental process for ensuring data quality and reliability, with critical applications in domains such as healthcare, finance, and commerce. A machine learning-based approach for optimizing record linkage in structured datasets is presented in this paper. By integrating hybrid blocking methods (combining standard blocking and sorted neighborhood approaches) with advanced similarity measures, computational overhead is significantly reduced while high accuracy is maintained. The performance of TabNet, a deep learning model designed for tabular data, is compared with traditional deep neural networks (DNNs) in the classification phase. Experimental results on a synthetic dataset of 5,000 records demonstrate that comparable precision and recall are achieved by TabNet to DNNs while execution time is reduced by over 79%. The scalability and efficiency of the proposed method are highlighted by these findings, making it well-suited for large-scale data management tasks. Practical and computationally efficient solutions for record linkage in the era of big data are contributed to by this work.
Co-Authors Abderrahim Waga Asmae Briouya Fatima Zahrae Saber Hasnae Briouya Mohamed Amnai Mouad Choukhairi Ouail Choukhairi Youssef Fakhri