KEYWORDS: Databases, Computer security, Deep learning, Information security, Associative arrays, Network security, Clouds, Data modeling, Control systems, Matrices
In order to solve the problem of database security intrusion identification and rationally plan the query path of database index, a method of database security intrusion identification based on mapping deep learning is proposed. A database security intrusion identification framework is constructed. Authorized users are grouped and calibrated according to the security level. The weights of plaintext keywords are obtained by mapping deep learning algorithm, and the keys of ciphertext are obtained. The encrypted results are uploaded to the main server, and the index of ciphertext is obtained by mapping deep learning algorithm, so as to realize database security intrusion identification. The experimental results show that the proposed method is more real-time for encrypting and decrypting a large number of data in the database, and the maximum recognition rate is 95%, which shows that the proposed method has good recognition effect.
KEYWORDS: Data modeling, Internet of things, Computer intrusion detection, Education and training, Gallium nitride, Neural networks, Deep learning, Machine learning, Evolutionary algorithms, Data processing
In recent years, the frequency and complexity of IoT network attacks have significantly increased. NIDS, strategically located in IoT network nodes, is an essential tool for monitoring traffic and detecting and mitigating network-based attacks. However, with the significant increase in computer network attacks, many datasets used for training suffer from imbalanced data problems. Therefore, to address the traffic characteristics of IoT networks and the issue of imbalanced data, this paper proposes an intrusion detection method that combines graph neural networks(E-GraphSAGE) and generative adversarial networks. Based on experiments using datasets NF-BoT-IoT, we found that training ML classifiers on datasets balanced with synthetic samples generated by WGAN-gp increased their prediction accuracy to 93.7% .
With the in-depth development of informatization, the Internet has become an important position for data security protection, an important network terminal of key infrastructure, and an important target for the infiltration and deep latent of hostile forces. In view of the risk of disclosure of important national and enterprise information caused by illegal transmission of important files by internal personnel, this paper studies the key technologies of content security based on NLP, and proposes a text classification method based on label tree, which effectively improves the accurate management of terminal data.
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