An Automated Vulnerability Assessment and Diagnostic Platform for Smart Camera Networks

With the exponential growth of the Internet of Things (IoT) ecosystem, smart IP cameras have become a ubiquitous component of modern surveillance infrastructure. However, these devices often prioritize functionality over security, making them a primary target for cyber-attacks. This research focuses on the design and development of an automated web-based framework dedicated to identifying security loopholes in smart cameras using their IP addresses.The proposed system operates by performing a non-intrusive network scan to discover active camera nodes and then executes a multi-layered vulnerability assessment. The methodology involves identifying open ports, service banners, and analyzing the underlying Real-Time Streaming Protocol (RTSP) for unauthorized access. Furthermore, the tool correlates the detected firmware versions against known Common Vulnerabilities and Exposures (CVE) databases to identify potential exploit vectors such as default credential vulnerabilities and cross-site scripting (XSS) flaws.Unlike generic network scanners, our implementation provides a specialized dashboard that categorizes risks into high, medium, and low levels for end-users. Experimental results conducted in a controlled environment demonstrate that the tool effectively identifies 92% of common misconfigurations in consumer-grade IP cameras. This project contributes to the field of proactive IoT security by providing a simplified yet robust tool for homeowners and administrators to audit their private surveillance networks, thereby mitigating the risk of unauthorized data breaches and privacy invasions.