Abstract
The proliferation of the Internet of Things (IoT) paradigm has ushered in a new era of connectivity and convenience. Consequently, rapid IoT expansion has introduced unprecedented security challenges , among which source code vulnerabilities present a significant risk. Recently, machine learning (ML) has been increasingly used to detect source code vulnerabilities. However, there has been a lack of attention to IoT-specific frameworks regarding both tools and datasets. This paper addresses potential source code vulnerabilities in some of the most commonly used IoT frameworks. Hence, we introduce IoTvulCode - a novel framework consisting of a dataset-generating tool and ML-enabled methods for detecting source code vulnerabilities and weaknesses as well as the initial release of an IoT vulnerability dataset. Our framework contributes to improving the existing coding practices, leading to a more secure IoT infrastructure. Additionally, IoTvulCode provides a solid basis for the IoT research community to further explore the topic.
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