Security identification of abnormal access to network databases based on GSA-SVM algorithm and deep features
Yuchen Jiang, Zhen Jia, Wenting Liu, Luyu Yang, Haitao Liu · 2024
In response to the problem of low success rate in identifying abnormal access security in network databases, a network database abnormal access security identification method based on GSA-SVM algorithm and deep features is proposed. By combining deep feature extraction technology and learning high-level abstract features from raw data, the accuracy and efficiency of anomaly access detection are effectively improved by utilizing the global optimization ability of GSA algorithm and the strong classification performance of SVM algorithm. The experimental results show that this method has high accuracy and stability in identifying abnormal access to network databases, providing an effective technical means for database security. This method has broad application prospects in the field of abnormal access detection in network databases, and can provide strong support for the security protection of network databases.