Integrating Deep Learning with Ensemble Approach for Anomaly Detection in Network Traffic

Intrusion Detection Systems (IDS) is frequently automated using expert systems and applied machine learning methods. Because of the interplay between different industrial control systems and the Internet environment that is inherent to IoT technology, critical infrastructure applications of communication technologies are vulnerable to attacks. Conventional machine learning techniques used in network anomaly detection are ineffective due to the massive network traffic present in crucial Cyber-Physical Systems. As a result, the detection and categorization of abnormalities at the network and host levels are successfully utilizing recently developed machine learning algorithms, with a focus on deep learning. It is very crucial in today's network traffic to distinguish between normal and malicious activities. With the rise of cyber threats, machine learning models have become popular for IDS due to their adaptability and high detection rates. This paper introduces a hybrid Network Intrusion Detection System (NIDS) that combines Long Short-Term Memory (LSTM) networks and XGBoost classifiers. By leveraging LSTM's temporal learning and XGBoost's boosting capabilities. The proposed model achieves 99% detection accuracy on the NF-BoT-IoT dataset, making it a robust solution for real-time anomaly detection.