NEXT-GEN-IDS: A DISTRIBUTED SMART NETWORK USING HYBRID AI MODELS

Abstract

The rapid expansion of Internet of Things (IoT) devices has introduced significant security challenges, creating a growing need for advanced Intrusion Detection Systems (IDS) capable of identifying cyber threats in real time. Traditional IDS solutions often struggle to cope with the dynamic and heterogeneous nature of modern IoT environments. As a result, Artificial Intelligence (AI)-based approaches have emerged as promising alternatives due to their ability to learn complex patterns and adapt to evolving threats. This study investigates the integration of Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy, efficiency, and adaptability of IDS in IoT networks. A Next Generation AI-based IDS is proposed to detect and classify various types of cyberattacks. The framework combines anomaly detection, behavioral analysis, and malicious pattern recognition to identify and mitigate security threats with minimal latency. To improve attack detection, a pre-trained Adaptive Recurrent Neural Network (A-RNN) is employed to effectively extract attack patterns from network traffic data. These extracted patterns are then processed using a hybrid Stacked Long Short-Term Memory (S-LSTM) and Convolutional Neural Network (CNN) architecture for accurate attack classification. The effectiveness of the proposed model is evaluated using two real-world datasets, namely the BETH Dataset and the IoT-23 Dataset. Experimental results demonstrate that the proposed AI-driven IDS achieves superior performance compared to existing approaches, highlighting its potential as a robust and efficient solution for securing IoT environments against emerging cyber threats.