Real-Time Intrusion Detection and Traffic Analysis Using AI Techniques in IoT Infrastructure

In light of the growing number of cyberattacks and the rapid boom in network traffic, it's crucial to discover network traffic anomalies or intrusions as they occur. Manual inspection is inefficient due to the massive volume, velocity, and variety of network traffic data. This research aims to broaden a clever, real-time anomaly detection framework using deep learning to know strategies within big statistics environments. The proposed technique makes use of multilayer feedforward neural networks, evaluated on benchmark intrusion datasets and large-scale actual network traffic, to pick out relevant traffic functions. This paper carries lengthy-short-term memory (LSTM) models and convolutional neural networks (CNN) based on packet seize facts. The model's overall performance is evaluated by the use of the G-Mean metric, which assesses the balance between sensitivity and specificity. The G-Mean is specifically beneficial in coping with imbalanced datasets through comparing the model's performance throughout both tremendous and terrible classes. The deep learning models demonstrated advanced anomaly detection talents as compared to conventional shallow gaining knowledge of strategies. This comprehensive method highlights the ability of deep learning for real-time anomaly detection, substantially improving network protection within huge statistics environments, including the Internet of Things (IoT).