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

Gayatri Ketepalli; srikanth yadav . M; K. N. S. Lakshmi; Saranya Eeday; Bharthavarapu Nirosha; Ragam Padmaja

doi:10.14419/1gb94217

Article Details

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

Authors:

Gayatri Ketepalli | srikanth yadav . M | K. N. S. Lakshmi | Saranya Eeday | Bharthavarapu Nirosha | Ragam Padmaja

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 5, pp.912-922

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001387470

pISSN:

2227-5053

DOI:

10.14419/1gb94217

Keywords:

Abstract:

 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.

Article Details

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

Authors:

Gayatri Ketepalli | srikanth yadav . M | K. N. S. Lakshmi | Saranya Eeday | Bharthavarapu Nirosha | Ragam Padmaja

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 5, pp.912-922

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001387470

pISSN:

2227-5053

DOI:

10.14419/1gb94217

Intrusion Detection System (IDS)

Network Anomaly Detection

LSTM Autoencoder (LSTMAE)

LightGBM Classifier

Feature Extraction

Issues

Article Details

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

Authors: Gayatri Ketepalli | srikanth yadav . M | K. N. S. Lakshmi | Saranya Eeday | Bharthavarapu Nirosha | Ragam Padmaja

Publisher: Science Publishing Corporation

Journal: International Journal of Basic and Applied Sciences Vol.14 Issue 5, pp.912-922

Document Type: Article

Publication Date: 9/2025

Document Language: English

Document ID (Org No.): 2001387470

pISSN: 2227-5053

DOI: 10.14419/1gb94217

Intrusion Detection System (IDS)

Network Anomaly Detection

LSTM Autoencoder (LSTMAE)

LightGBM Classifier

Feature Extraction

Issues

Authors:

Gayatri Ketepalli | srikanth yadav . M | K. N. S. Lakshmi | Saranya Eeday | Bharthavarapu Nirosha | Ragam Padmaja

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 5, pp.912-922

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001387470

pISSN:

2227-5053

DOI:

10.14419/1gb94217