Design of a Smart Camera Vulnerability Detection System Based on Homology Analysis

In recent years, the number of smart cameras has grown exponentially, and the number of attacks targeting them has also increased. Many of these devices lack adequate security mechanisms and connect to networks with vulnerabilities, creating significant pressure on the overall network environment. Traditional vulnerability scanning tools typically rely on simple feature matching methods for vulnerability detection, and they often perform redundant scans on devices with similar characteristics. This not only results in low efficiency but also leads to a higher false positive rate. This paper proposes a smart camera vulnerability scanning system based on device homogeneity analysis. By quickly identifying security vulnerabilities in devices with the same architecture and components through homogeneity analysis, and leveraging machine learning algorithms to enhance the accuracy of vulnerability detection, the system significantly improves scanning efficiency and reduces false positives.