Title: Enhancing intrusion detection through anomaly detection and integrated deep learning with TabTransformer

Authors: Omar Al-Harbi

Addresses: Applied College, Jazan University, Jazan, Saudi Arabia

Abstract: Intrusion detection systems (IDSs) are crucial for safeguarding network infrastructures as cybersecurity threats rapidly evolve, necessitating effective detection and response mechanisms. This paper introduces an intrusion detection model that combines anomaly detection with deep learning techniques and the TabTransformer architecture. Anomaly detection performs binary classification, identifying deviations from normal behaviour and enriching the feature set with binary predictions. For multiclass classification, the TabTransformer processes categorical data efficiently, while the convolutional neural network (CNN) and long short-term memory (LSTM) extract patterns from continuous data. Evaluations on benchmark datasets demonstrate that the proposed method surpasses baseline models, achieving superior accuracy, recall rate, and false positive rate.

Keywords: intrusion detection; TabTransformer; anomaly detection; deep learning; convolutional neural network; CNN; long short-term memory; LSTM.

DOI: 10.1504/IJSNET.2025.147642

International Journal of Sensor Networks, 2025 Vol.48 No.3, pp.166 - 177

Received: 24 Feb 2025
Accepted: 03 May 2025
Published online: 24 Jul 2025 *

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