Abstract:

The rapid proliferation of Internet of Things (IoT) devices has transformed industries by expanding connectivity, yet it has also exposed significant security vulnerabilities. This research introduces an innovative automated security assessment framework designed specifically for IoT networks. The framework employs cutting-edge technologies like machine learning and natural language processing to proactively identify and mitigate vulnerabilities. This paper provides a comprehensive overview of the framework's development process. It begins with a detailed methodology covering problem definition, data collection strategies, preprocessing techniques, and the integration of machine learning algorithms. The importance of high-quality data from reputable sources, including vulnerability databases and IoT device specifications, is emphasized. The core of the framework's predictive capabilities is the integration of advanced machine learning techniques, such as natural language processing, feature extraction, and model training. These techniques create structured representations of vulnerability descriptions, forming the basis for effective vulnerability prediction. Additionally, the paper presents a thorough evaluation process, including methodology, dataset partitioning, and results. The framework's predictive accuracy, its ability to identify critical attack paths, and its overall effectiveness in enhancing IoT network security are rigorously assessed.

Description:

Internet of Things, security assessment, vulnerability, machine learning, automated framework