One of the most persuasive technologies in developing universities' smart campus applications is the Internet of Things (IoT) technique. Deploying thousands of readily available devices connected to IoT systems by ignoring device vulnerabilities and threat strategies in smart campus infrastructure is exacerbating security challenges. Moreover, unreliable sensing, transmission, or processing of IoT devices, false observations, long delays, and data reports reveal the vulnerability of efficient smart campus infrastructure. Some transient errors or attacks also occur here due to many vulnerable device memory, processing power, soft errors, and battery imperfections. The need to overcome significant challenges, including advanced training-rich IoT devices, credible designers, reliability, scalability, interoperability, availability, and performance, has motivated our aim to implement intelligent platforms for university campuses. In this study, we propose an operational framework for smart campuses to detect and mitigate vulnerabilities aimed at processing a comprehensive security certification of IoT devices, including introducing a smart model for university campuses. We discuss challenges, detection, and mitigation of vulnerabilities associated with smart campuses. From the literature exploration, we found that machine learning and DNN are capable of being used to detect malicious behaviour and vulnerable sources. Thus, the proposed framework is expected to provide better security and be capable of meeting the compliance of existing university services. [For the full proceedings, see ED654100.]