异常检测在网络安全防护中的应用研究

Research on Application of Anomaly Detection Algorithm in Network Security Protection

图像异常数据作为网络安全检测的核心监控对象,面临着样本不均衡、数据缺乏标注以及异常形式多样化等挑战,针对这些问题,创新性地提出了自信息量挖掘模块,旨在学习已知类别样本的数据模式;同时提出了三元组信息量学习策略,优化类别信息学习和已知类别的数据模式学习,最终实现了在网络安全防护场景中对图像的未知类别样本的异常检测。实验结果表明,异常检测算法可以有效提升网络安全防护的准确性,在实际应用中表现出色。

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.