Leveraging Convolutional Neural Network (CNN)-based Auto Encoders for Enhanced Anomaly Detection in High-Dimensional Datasets
Received: 8 August 2024 | Revised: 29 August 2024 and 20 September 2024 | Accepted: 22 September 2024 | Online: 5 October 2024
Corresponding author: Hamayun Khan
Abstract
This study presents an Auto-Encoder Convolutional Neural Network (AECNNs) approach for anomaly detection in high-dimensional datasets. Unsupervised learning-based algorithms have a strong theoretical foundation and are widely used for anomaly detection in high-dimensional datasets, but some limitations significantly reduce their performance. This study proposes an algorithm to address these limitations. The proposed AECNN combines various convolutional layers, feature extraction, dimensionality reduction, and data preprocessing and was evaluated using accuracy, precision, recall, and F1-score. The performance of the proposed model was evaluated using a large real benchmark dataset. The proposed CNN-based autoencoder distinguished anomalies with an AUC score of 0.83 and remarkable accuracy, precision, recall, and F1 score.
Keywords:
autoencoders, anomaly detection, high-dimensional data, machine learning, data analysis, model evaluation, Convolutional Neural Networks (CNNs), NSL-KDD, UNSW-NB15, MSEDownloads
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Copyright (c) 2024 M. Aetsam Javed, Madiha Anjum, Hassan A. Ahmed, Arshad Ali, H. M. Shahzad, Hamayun Khan, Abdulaziz M. Alshahrani
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