Business Faculty Publications

Document Type

Article

Publication Date

9-22-2024

Publication Title

Engineering, Technology & Applied Science Research

Keywords

autoencoders; anomaly detection; high-dimensional data; machine learning; data analysis; model evaluation; Convolutional Neural Networks (CNNs); NSL-KDD; UNSW-NB15; MSE

Disciplines

Business | Computer and Systems Architecture

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.

Version

Publisher's PDF

Volume

14

Issue

6

Share

COinS