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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Idouglid, Lahcen
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience Idouglid, Lahcen; Tkatek, Said; Elfayq, Khalid; Guezzaz, Azidine
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.pp3512-3521

Abstract

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.
Predicting television programs success using machine learning techniques Fayq, Khalid El; Tkatek, Said; Idouglid, Lahcen
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5502-5512

Abstract

In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.
Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques Idouglid, Lahcen; Tkatek, Said; Elfayq, Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1232-1241

Abstract

The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
Enhancing TV program success prediction using machine learning by integrating people meter audience metrics with digital engagement metrics El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp353-363

Abstract

With the emergence of numerous media services on the internet, television (TV) remains a highly demanded medium in terms of reliability and innovation, despite intense competition that compels us to devise strategies for maintaining audience engagement. A key indicator of a TV channel’s success is its reach, representing the percentage of the target audience that views the broadcasts. To aid TV channel managers, the industry is exploring new methods to predict TV reach with greater accuracy. This paper investigates the potential of advanced machine learning models in predicting TV program success by integrating people meter audience metrics with digital engagement metrics. Our approach combines convolutional neural networks (CNNs) for processing digital engagement data, long short-term memory (LSTM) networks for capturing temporal dependencies, and gaussian processes (GPs) for modeling uncertainties. Our results demonstrate that the best-performing hybrid model achieves a prediction accuracy of 95%. This study contributes to the field by addressing manual scheduling errors, financial losses, and decreased viewership, providing a more comprehensive understanding of audience behavior and enhancing predictive accuracy through the integration of diverse data sources and advanced machine learning techniques.
Enhanced pre-broadcast video codec validation using hybrid CNN-LSTM with attention and autoencoder-based anomaly detection El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2864-2875

Abstract

This study presents a machine learning-based approach for proactive video codec error detection, ensuring uninterrupted television broadcasting for TV Laayoune, part of Morocco’s SNRT network. Building upon previous approaches, our method introduces autoencoders for improved anomaly detection and integrates data augmentation to enhance model resilience to rare codec configurations. By combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with an attention mechanism, the system effectively captures spatial and temporal video features. This architecture emphasizes critical metadata attributes that influence video playback quality. Embedded within the broadcasting pipeline, the model enables real-time error detection and alerts, minimizing manual intervention and reducing transmission disruptions. Experimental results demonstrate a 97% accuracy in detecting codec errors, outperforming traditional machine learning models. This study highlights the transformative role of machine learning in broadcasting, enabling scalable deployment across diverse television networks.
Co-Authors El Fayq, Khalid Elfayq, Khalid Fayq, Khalid El Guezzaz, Azidine Tkatek, Said