Analysis Of Vulnerabilities In Communication Channels Using An Integrated Approach Based On Machine Learning And Statistical Methods

This paper investigates potential weaknesses in embedded systems communication channels, with a focus on identifying and analyzing specific vulnerabilities. The problem of securing communication in embedded systems is critical due to the increasing reliance on these systems in various industries, from automotive to healthcare, where breaches can lead to significant safety and security risks. To address this issue, the study employs a multi-faceted approach using machine learning and statistical methods for vulnerability detection. Machine learning techniques, such as supervised learning algorithms, were utilized to identify patterns indicative of potential security breaches. Additionally, Markov Chains were applied to model and analyze the probabilistic behavior of communication sequences, identifying anomalies that could signify vulnerabilities. A practical application was developed using virtual instrumentation in LabVIEW, providing a user-friendly interface for real-time vulnerability detection and analysis. The results demonstrate that the combined use of machine learning and statistical methods significantly improves the accuracy and efficiency of detecting vulnerabilities in embedded systems communication channels. This research highlights the importance of integrated security measures and presents a robust framework for enhancing the security of embedded systems.