| Abstract |
In the digital world where the smart device is a ubiquitous feature of modern life, embedded systems are almost everywhere. From the smart home, to the smart office, to the smart restaurant, and even the smart traffic system, this technology has become a crucial aspect of the modern experience. Although these systems are typically quite efficient, the rapid growth of embedded systems in smart cities has created inconsistencies and misalignments between secured and unsecured systems, which has created a need for secure, invulnerable embedded systems to protect modern users from the dangers of hacking. To address this problem, we have developed a large, novel, and unbiased dataset for embedded system vulnerability detection and modifying an advanced machine learning model for Linux Kernel. This unique dataset will provide the landscape of embedded systems with a multitude of ways to predict the vulnerabilities in the Linux Core and to detect all vulnerabilities during the development process. This research paper discusses the collection, filtering, correlation, and statistics associated with the dataset for Linux Embedded system. © 2022 IEEE. |
