Some sensitive data in the network will be leaked due to the loopholes or weaknesses of the web system itself, which will bring potential harm to the society or the public. Aiming at this, this study carries out the design of web security penetration test system. A test scheme comparing single method with an automatic comprehensive test method is designed. Based on this scheme, an automatic penetration test system script used under the terminal operation page is tested and designed. A security evaluation algorithm based on the prediction results of the game between attack and defense is proposed. Through this algorithm, different website systems are evaluated and scored, and the test results are compared through scoring. The automatic penetration test integration system designed and implemented in this study can meet the main objectives of web security and the protection requirements of websites against general, routine, and universal security attacks. The proposed evaluation algorithm is more detailed, accurate, and reference in scoring.

High-dimensional data usually exist asymptotically in low-dimensional space. In this study, we mainly use tensor t-product as a tool to propose new algorithms in data clustering and recovery and verify them on classical data sets. This study defines the “singular values” of tensors, adopts a weighting strategy for the singular values, and proposes a tensor-weighted kernel norm minimization robust principal component analysis method, which is used to restore low-probability low-rank third-order tensor data. Experiments on synthetic data show that in the recovery of strictly low-rank data, the tensor method and weighting strategy can also obtain more accurate recovery when the rank is relatively large, which improves the volume of the rank. The proposed method combines the two and reflects its superiority through the restoration of 500 images under a small probability noise level.

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