Research on Intrusion Detection Algorithm for Internet of Things Devices Based on Deep Learning
Zhao Liu, Xiaoqing Xu · 2025
Amidst the burgeoning proliferation of the Internet of Things, the safeguarding of Internet of Things (IoT) devices confronts formidable challenges, rendering intrusion detection of paramount significance. This treatise delves into the scrutiny of intrusion detection algorithms for IoT devices grounded in deep learning. In the course of the research, an extensive quantum of both normal and aberrant operational data of IoT devices was initially amassed to construct a dataset. The CNN serves to extricate the local idiosyncrasies of the data, whereas the LSTM processes the temporal sequence information of the data to capture the dynamic vicissitudes in the operation of IoT devices. Through the training, validation, and optimization of the model, the parameters were calibrated to enhance the detection veracity. The experimental outcomes evince that this algorithm can efficaciously discern diverse intrusion comportments of IoT devices. In comparison to traditional intrusion detection algorithms, it exhibits superior detection accuracy and robustness within intricate network environs, proffering a reliable resolution for the security fortification of IoT devices.