A Hybrid Intrusion Detection Model Using LSTMAE and ‎LightGBM for Robust Anomaly Detection in Network Systems

Traditional intrusion detection systems often face challenges such as low accuracy and high false positive rates, particularly when detecting ‎complex and evolving cyber threats. To address these limitations, a hybrid intrusion detection model is developed by integrating Long ‎Short-Term Memory Autoencoder (LSTMAE) with Light Gradient Boosting Machine (LightGBM). The LSTMAE component captures ‎temporal dependencies in network traffic, enabling effective feature extraction, while LightGBM performs efficient classification of the ex‎extracted features. The model is evaluated on benchmark datasets including NSL-KDD, UNSW-NB15, and CICIDS2017, following comprehensive preprocessing to ensure data consistency. Experimental results demonstrate that the hybrid model significantly outperforms ‎standalone classifiers and conventional methods, achieving improved detection accuracy and reduced false positive rates. These findings ‎highlight the model’s effectiveness in differentiating between normal and malicious traffic, offering a scalable and efficient solution for real-‎time intrusion detection, and laying the groundwork for future ensemble-based security frameworks‎.