基于深度学习的物联网入侵检测系统综述OA

While the interconnection of smart devices in the Internet of Things promotes social progress,it also leads to increas-ingly complex security threats due to device heterogeneity,protocol diversity and resource constraints.Traditional intrusion detec-tion systems rely on feature matching and rule definition,and show limitations when facing new attacks and dynamic attack pat-terns.This paper systematically sorts out the application progress of deep learning technology in the intrusion detection system of the Internet of Things.Through comparative analysis,it is found that the model based on deep learning is superior to traditional methods in detection accuracy and real-time performance,and has outstanding performance in processing spatial features and cap-turing temporal dependencies.Unsupervised learning and integration methods effectively improve the detection robustness in small sample scenarios by generating adversarial samples and integrating the advantages of multiple models.Current research still faces challenges such as high data annotation costs,limited edge computing resources,and insufficient adaptability to dynamic attacks.This paper summarizes and discusses the directions that future research should focus on,such as lightweight and cross-modal data fusion,to provide theoretical support for building an efficient and adaptive Internet of Things security protection system.