Construction of a Smart City Network Information Security Evaluation Model Based on GRA-BPNN

The emergence of mobile medical social networks has provided great convenience for patients to communicate with each other about their medical conditions, promoting efficient and high-quality communication and exchange among patients. However, at the same time, it has also raised issues of confidentiality and privacy of patient data. In response to this issue, this article proposes a cloud-based attribute-based signature encryption scheme that can effectively protect the privacy of patient data. The patient encrypts their medical information and uploads it to the cloud server. When the data user wants to access the patient’s information, the cloud server helps the data user partially decrypt and verify the integrity of the data, which to some extent reduces the computational workload of the data user. Meanwhile, under the random oracle model, it has been proven that the scheme satisfies the unforgeability under selective message attacks, indistinguishability under selective cipher text attacks, and attribute privacy security. Theoretical analysis and numerical simulation experiments show that this scheme has higher efficiency than existing schemes in the signing and decryption stages.

Section: System Architecturementioning

confidence: 99%

Attribute Based Signature Encryption Scheme Based on Cloud Computing in Medical Social Networks

2024

“…Further improving the efficiency of network security situation recognition and the accuracy of prediction is still an important aspect that needs to be studied. In the process of continuous development and improvement of modern artificial intelligence technology, LSTM neural network, as an improvement of RNN neural network, can improve the efficiency of feature recognition in network security situation prediction, further improve the accuracy of model prediction, and lay the foundation for the evaluation of network security situation [8,9].…”

Section: Introductionmentioning

confidence: 99%

Construction and Analysis of QPSO-LSTM Model in Network Security Situation Prediction

Wentao

2024

The continuous improvement of artificial intelligence technology has deepened its application in many fields and provided more support for predicting network security situations. QPSO-LSTM model based on LSTM neural network and fused with QPSO algorithm provides more options for improving network security situation prediction, further enhancing the effectiveness of network security situation prediction, and enabling more efficient and accurate prediction and analysis of network security situations. By comparing the applications of different types of algorithms in network security situation prediction, it was found that the QPSO-LSTM model has smaller prediction errors, can achieve higher prediction accuracy, and can also obtain higher F1-score and AUC values; the shorter identifying runtime also lays the foundation for improving the speed and efficiency of network security situation prediction. Therefore, in the field of network security situation prediction, the application of QPSO-LSTM model can provide more support for further improvement and improvement of security situation prediction performance in this field.

“…Campus network security management optimizes the management mode and improves the management level with the help of high technology, which can provide faster and better services. Users solve the difficulties in life; finally, comprehensive, grid-based network security management further refines different campus units, broadens the service area, and makes campus services more comprehensive and complete [14].…”

Section: Introductionmentioning

confidence: 99%

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Chen

2024

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability