Forecasting Multi-Level Deep Learning Autoencoder Architecture (MDLAA) for Parametric Prediction based on Convolutional Neural Networks

Authors

  • Nasir Ayub Department of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, Pakistan
  • Nadeem Sarwar Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan
  • Arshad Ali Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, Saudi Arabia
  • Hamayun Khan Department of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, Pakistan
  • Irfanud Din Department of Computer Science, New Uzbekistan University, Tashkent, Uzbekistan
  • Abdullah M. Alqahtani College of Engineering & Computer Science, Department of Electrical & Electronic Engineering, Jazan University, Jazan, Saudi Arabia
  • Mohamed Shabbir Hamza Abdulnabi Network Security Forensic Group, School of Technology, Asia Pacific University, Malaysia
  • Aitizaz Ali School of Technology, Network Security Forensic Group, Asia Pacific University, Malaysia
Volume: 15 | Issue: 2 | Pages: 21279-21283 | April 2025 | https://doi.org/10.48084/etasr.9155

Received: 2 October 2024 | Revised: 9 December 2024 and 12 December 2024 | Accepted: 18 December 2024 | Online: 11 February 2025


Corresponding author: Mohamed Shabbir Hamza Abdulnabi

Abstract

This study presents a data-driven framework for anomaly detection, which is a significant process in modern computing, as the detection of an abnormal signal can prevent a high-risk decision. The proposed Multi-Level Deep Learning Autoencoder Architecture (MDLAA) is used to encode high dimensional input data using CNNs for anomaly detection in High Dimensional Input Datasets (HDDs). MDLAA is based on unsupervised learning, which has a strong theoretical foundation and is widely used for the detection of anomalies in HDDs, but a few limitations significantly reduce its performance. The proposed MDLAA combines multilevel convolutional layers and data preprocessing. The performance of the proposed model was evaluated on a benchmark dataset. Using feature engineering, the proposed algorithm assists in the detection of anomalies that are present in data structures, especially when compared to the ResNet101 feature extractor. The results show that given adequate data, the proposed technique outperformed other previously implemented deep learning approaches and classification models, showing an overall improvement of 2.3% in terms of MSE, F1-score, precision, and accuracy.

Keywords:

Convolutional Neural Networks (CNNs), NSL-KDD, autoencoders, UNSW-NB15, anomaly detection, image classification, machine learning, data analysis

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How to Cite

[1]
Ayub, N., Sarwar, N., Ali, A., Khan, H., Din, I., Alqahtani, A.M., Abdulnabi, M.S.H. and Ali, A. 2025. Forecasting Multi-Level Deep Learning Autoencoder Architecture (MDLAA) for Parametric Prediction based on Convolutional Neural Networks. Engineering, Technology & Applied Science Research. 15, 2 (Apr. 2025), 21279–21283. DOI:https://doi.org/10.48084/etasr.9155.

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Copyright (c) 2025 Nasir Ayub, Nadeem Sarwar, Arshad Ali, Hamayun Khan, Irfanud Din, Abdullah M. Alqahtani, Mohamed Shabbir Hamza Abdulnabi, Aitizaz Ali

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