Next-Gen IDS: Advanced AI for Real-Time Threat Detection in Smart Multiple Networks

The fast growth of IoT devices brings new security challenges that require a next-generation Intrusion Detection System (Next-Gen-IDS), which can detect real-time information security threats. Artificial Intelligence (AI) solutions are a viable candidate to address the shortcomings of conventional IDS; however, the performance of these solutions will largely depend on the nature and volume of the data collected in smart IoT environments, which are often dynamic and heterogeneous. This paper analyzes the integration of AI methodologies like Machine Learning (ML) and Deep Learning (DL) to improve the accuracy, efficiency, and adaptability of IDS in IoT networks. In this work, the types of attacks are detected and classified by a Next-GEN AI IDS. An innovative combination of anomaly detection, behavior analysis, and finding malicious patterns is utilized to detect and mitigate cyber adversaries with negligible latency. The proposed approach is the combination of a pre-trained model Adaptive Recurrent Neural Network (A-RNN) to find the attack patterns effectively and transforms these patterns to the proposed approach Stacked Long Short-Term Memory (S-LSTM) - Convolutional Neural Network (CNN) for effective classification of attacks. The strength of the proposed approach applied on two real time datasets such as BETH Dataset, and IoT −23 Dataset are used for experimental analysis. Finally, the proposed approach obtains superior results compared with the existing models.