Enhanced Hybrid Intrusion Detection System with Attention Mechanism using Deep Learning

The introduction of the Attention mechanism by the Internet of Things—or WSN-IoT—in the sector has greatly enhanced the intrusion detection mechanism capabilities, whereas the deep learning techniques, together with the attention mechanism, enhance the efficacy and efficiency of IDS within WSNs. The proposed “Enhanced Hybrid Intrusion Detection System with Attention Mechanism” (EHID-SCA) underlying insight of this is that the characteristics of WSN data validate this including Convolutional Neural Networks (CNNs) and other deep learning models into an effective and coherent architecture. The design of the deep hybrid network with attention incorporates Channel Attention and Spatial Attention as some of the main components. The model biased capacity is enlarged to choose and stress important spatial and channel information from the sensor input. The method is based on the consideration of these things and it cuts noise. Therefore, the proposed technique can be made to pave the way for enabling the Internet of Things intrusion detection systems to do the automatic extraction of useful information from sensor data through the utilization of deep learning along with the Attention mechanism. The attack might therefore be better situated for mitigation in the network design that allows analysis of the geographical and temporal context of events, which are then more properly termed as intrusion events.