Networked Test System Attack Detection Based on Deep Generative Models.

With the widespread application of networked test systems in aviation and other fields, ensuring the safety and reliability of these systems is of great importance. This paper proposes an innovative attack detection model, which combines the improved density peak clustering algorithm (mdpca) and deep confidence network (DBN), and is named mdpca-dbn. The model aims to address various security challenges that networked test systems may face, including data theft, tampering, and interference during the test process. By integrating density peak clustering and deep confidence network, the model can better identify potential security threats, and has shown satisfactory performance in experiments. The introduction of this model is expected to enhance the security of networked test systems and ensure the accuracy and reliability of test results. In the future, we will continue to improve and optimize this model to adapt to evolving network security threats.