Unified Data Handling for Vulnerability Detection and Mitigation in Smart Systems

A new era of connectedness and simplicity has arrived because of the Internet of Things (IoT) adoption. However, this interconnectedness also introduces significant security challenges, as IoT devices are susceptible to various vulnerabilities that malicious users can exploit. Traditional vulnerability discovery methods often fall short in addressing the dynamic and heterogeneous nature of IoT ecosystems. In this paper, we propose an integrated approach that combines data preprocessing, cleaning, and reduction techniques to enhance vulnerability discovery and mitigation in smart systems. We begin by discussing the importance of each of these data processing steps and their role in preparing raw data for analysis. Subsequently, we explore specific methodologies for data preprocessing, cleaning, and reduction, tailored to the unique characteristics of smart system data. We then illustrate how these techniques can be seamlessly integrated into a comprehensive framework for vulnerability analysis, from data ingestion to mitigation strategy formulation. Through case studies or experimental validation, we demonstrate the efficacy of our proposed approach in improving the efficiency and accuracy of vulnerability discovery and mitigation efforts. Overall, this paper contributes to the advancement of security measures in smart systems by providing a systematic and holistic approach to data-driven vulnerability management. This comprehensive approach ensures data consistency, integration, and correctness, ultimately enhancing the reliability and security of smart systems.