SecDataGen: AI-Driven Security Data Synthesis for IoT Ecosystems

IoT and CPS have become integral parts of our lives. The enormous amount of data generated by various IoT devices across different applications has amplified the need for AI to process this information. This fusion of AI and IoT has led to the emergence of Artificial Intelligence of Things (AIoT) systems. For the successful implementation and continuous operation of AIoT systems in these intelligent spaces, access to realistic and reliable data at scale is essential. However, obtaining such data, especially data that represents the off-nominal behavior of the system, poses a significant challenge.

This is where SecDataGen steps in, aiming to address this challenge. SecDataGen leverages the power of AI to generate synthetic data that can be utilized by other AI services. This ensures the availability of trustworthy datasets, catering to the diverse needs of AIoT systems, particularly in scenarios where security is of utmost importance.

Keywords: CyberSecurity, AI, IoT, Artificial Intelligence of Things

Our goals

  • Trustworthy Dataset Generation: the main goal of SecDataGen is to generate datasets that accurately represent various scenarios in AIoT systems, including off-nominal behaviors. SecdataGern will utilize AI-based methods to generate synthetic data generation.

  • AI Model Improvement: Using the generated dataset, we aim to enhance the accuracy of the AI models used in AIoT systems. This is done by utilizing the information gathered from the deployed system during its operation, which helps refine the AI model used for data generation in other systems.

  • Security Enhancement: By leveraging AI, SecDataGen aims to ensure the availability of trustworthy datasets, which is crucial in scenarios where security is a paramount concern.

Synthetic data generation techniques have the potential to revolutionize pervasive systems, offering significant advancements in security and privacy measures

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