Data Fusion-Based Machine Learning Architecture for Intrusion Detection

In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices' connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any intrusion activity. Data fusion is a well-known methodology that can be beneficial for the improvement of data accuracy, as well as for the maximizing of wireless sensor networks lifespan. We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective. By using the Real Time Sequential Deep Extreme Learning Machine (RTSDELM) methodology, an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished. Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach. Eventually, threats and a more general outlook are explored.