Research on Application of Anomaly Detection Algorithm in Network Security Protection
Abnormal image data,as the core monitoring target of network security detection,faces challenges such as sample imbalance,lack of data annotation,and diverse forms of abnormalities.To address these issues,it innovatively proposes a self-information mining module aimed to learn the data patterns of known-category samples.Simultaneously,a triplet information learning strategy is introduced to optimize category information learning and known-category data pattern learning,ultimately enabling the detection of abnormalities for unknown class samples of images in the context of network security protection.Experimental results show that the anomaly detection algorithm can effectively improve the accuracy of network security protection,demonstrating excellent performance in practical applications.
Deep learningAnomaly detectionNetwork securityData pattern
NETL
NSTL
万方数据
