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An Automatic Vulnerability Classification System for IoT Softwares

Internet of Things(IoT) have been widely implemented in diverse domains of real-life, and become one of the most popular applications of the internet. Nevertheless, the development of IoT has suffered from its security issues so far. Various IoT vulnerabilities bring serious risks to the privacy and the property security of users. To study the security vulnerabilities in depth, the classification for IoT vulnerabilities becomes a basic requirement. However, manual classification relies on human experience and is very laborious. In this paper, an IoT vulnerabilities classification system based on Support Vector Machines(SVM) and Particle Swarm optimization(PSO) is developed to identify and classify IoT vulnerabilities automatically. The experimental results prove that our system presents great potential of vulnerability classification.