Sequence‑Aware Hybrid LSTM‑Transformer Intrusion Detection on a Custom Packet‑Capture Dataset

다국어 초록 (Multilingual Abstract)

As cyber threats continue to evolve in sophistication, there is urgent need for intelligent, adaptive and context aware intrusion detection systems. In this paper, we present an intrusion detection framework that employs deep learning models to detect anomalies in network traffic using custom dataset. The dataset was constructed in a controlled lab environment using various intrusion attack scenarios such as DoS, SSH abuse and VPN exploitation. Deep learning models were then applied to detect the intrusions. The performance of the models in performing detection tasks were evaluated using metrics of accuracy, precision, recall and F1- score. The results that were obtained indicate that hybrid model achieved the best results with overall accuracy of 0.99 followed by transformer (0.98) and LSTM model (0.97) being the last. This study highlights the potential of leveraging well designed custom IDS datasets and deep learning techniques to enhance intrusion detection mechanisms thereby providing a robust framework for intrusion detection applications.