In the information age, hackers will use Web vulnerabilities to infiltrate websites, resulting in many security incidents. To solve this problem, security-conscious enterprises or individuals will conduct penetration tests on websites to test and analyze the security of websites, but penetration tests often take a lot of time. Therefore, based on the traditional Web vulnerability scanner, the Web vulnerability detection analyzer designed in this article uses vulnerability detection technologies such as sub-domain scanning, application fingerprint recognition, and web crawling to penetrate the website. The vulnerability scanning process of the website using log records and HTML output helps users discover the vulnerability information of the website in a short time, patch the website in time. It can reduce the security risks caused by website vulnerabilities.

Aiming at the low recognition accuracy caused by the problems of angle, illumination, and occlusion in vehicle re-identification based on deep learning, a vehicle re-identification method based on multibranch network feature extraction and two-stage retrieval feature is proposed. The multibranch feature extraction module uses ResNet-50 as the backbone network to extract the vehicle’s attribute features and apparent features, respectively, and uses the attribute features for rough retrieval. On this basis, the attribute features and apparent features are fused for fine retrieval. Through experiments, the accuracy of vehicle re-identification on Veri-776 data set and VehicleID datasets is significantly improved. In addition, based on the improved algorithm, this paper designs and develops a vehicle re-identification system, which realizes the functions of inputting file directory, selecting target image, and querying result image, and provides a visual technical scheme for vehicle re-identification and retrieval in the real scene.