Vulnerability detection of IoT information cloud interaction process under penetration testing
Lei Yun, Dan Li · 2024
The Internet of Things technology makes a large number of devices connected to the Internet, resulting in massive interactive data. However, data is susceptible to hacker attacks and malicious exploitation during the interaction process, resulting in serious data leakage and privacy issues. Penetration testing can simulate real attack behavior, help identify vulnerabilities and security vulnerabilities in the process of IoT information cloud interaction, and prevent malicious attackers from exploiting vulnerabilities to invade systems and obtain sensitive information. Therefore, vulnerability detection in the process of IoT information cloud interaction under penetration testing is proposed. By evaluating the defense mechanism status of the Internet of Things through bidirectional penetration, a machine learning model based on time Markov vector features is constructed. The model is trained using historical session flow data to identify session flows containing sensitive information. At the same time, hidden Markov models are used to classify normal information interaction behavior samples to detect security vulnerabilities during the information interaction process. The experimental results show that the success rate of using the proposed method for vulnerability detection in the process of IoT information cloud interaction remains above 98%. Relatively high, it can accurately detect vulnerabilities generated during the interaction process of IoT information cloud, ensuring the security of IoT.