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: Robots, Network security, Information security, Control systems, Power grids, Clouds, Matrices, Defense and security, Telecommunications, Monte Carlo methods
The information network of the power grid enterprise is the same as the information system of the common Internet, and its application layer has a lot of general IT software. In order to mine all kinds of software vulnerabilities timely and ensure the normal operation of the software system, the most obvious disadvantage of this method is the low efficiency of implementation, which requires manual dynamic testing after the end of software development. It also needs to track the location of vulnerabilities according to the test results. In this paper, the cooperative control of heterogeneous wireless networked robots based on parallel control is proposed, and the configuration of defense resources is studied from the perspective of protection. Firstly, based on the network security robot, the game model of both sides of the power grid attack and defense under coordinated attack is established, and the optimal defense resource allocation strategy is analyzed and solved. Then, for heterogeneous wireless networks, a step-by-step solution is proposed based on parallel control optimization, and the allocation method of defense resources is formulated. Finally, the proposed method is verified on the simulation test system and compared with AUKF and IMM-UKF. The experimental results verify that the proposed method performs well in error control when the execution state changes, and achieves high accuracy, good stability and strong security prediction ability in general. It can ensure the safety of power grid and promote the healthy development of the national power industry.
KEYWORDS: Data modeling, Data fusion, Machine learning, Education and training, Feature fusion, Data conversion, Data communications, Data analysis, Data acquisition
The rapid development of edge network devices has led to the explosive growth of their data, and the difficulty of dealing with heterogeneous data in edge devices has been further increased. To solve the problem of heterogeneous data fusion without interaction, this paper proposes a data heterogeneous model analysis based on federated learning. Preprocess the multi-source heterogeneous data to obtain the main features of the condensed data. Then, the multi-source heterogeneous data nodes are positioned to avoid multi-fusion results, and Spatio-temporal correlation degree of the multi-source heterogeneous data is calculated to improve the accuracy of fusion. Finally, a multi-source heterogeneous data fusion model is established based on federated learning to ensure the security of data fusion. Compared with the traditional model, the data fusion of the proposed model is more stable, and the error is smaller. The effectiveness of the proposed model is verified by the stability and accuracy of the fusion of the heterogeneous data. The multi-source heterogeneous data fusion model studied in this paper can improve the quality of Internet of Things data and promote the development of edge devices in China.
KEYWORDS: Computer security, Network security, Data transmission, Databases, Reliability, Information security, Data communications, Internet technology, Internet, Head
In recent years, privacy data has been stolen frequently, and the security of privacy data has been concerned by society. In the big data environment, privacy is facing unprecedented challenges, and some traditional privacy protection technologies are facing failure, so how to choose a reasonable privacy protection technology is a challenging task. To solve the problem of a large amount of data theft in the application of traditional methods in the privacy data security protection, this paper proposes research on privacy data security based on multi-party computation. The hash function is used to encrypt the private data, and the data is stored in the block chain in the form of a private data encryption file. Based on the theory of multi-party computation, a secure multi-party technology protocol is designed, and the protocol is used to verify the identity of the participants in the private data transmission to realize the security protection of private data. The experimental results show that the amount of privacy data stolen by the application design method is less than that of the traditional method, which has important application value for the security protection of privacy data.
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