Developing Security Recommender System Using Content-Based Filtering Mechanisms

Machine learning and artificial intelligence are becoming more common today. They are used in a variety of areas, including the energy, medical, and financial sectors, to complete different tasks and assist in the making of key choices. Among other uses, machine learning and artificial intelligence are used to build powerful recommender engines to provide user with corresponding recommendations in different directions like movie recommendations, friends suggestions in social networks and much more. The goal of the scientific work offered by the authors of this research was to identify and understand the vulnerabilities of hardware-based systems and related mechanisms, in order to improve the corresponding security measures. The main goal of the offered research is to design an upgraded recognition system and to identify the corresponding hardware-based vulnerabilities. Based on the research the goal of the papers is also to provide the potential users with the corresponding recommendations. This article discusses a web-based system that studies the potential security issues in hardware-based systems and provides optimal solutions. The system was tested using real-world cases from industrial and corporate organizations, and the assessment process demonstrates its ability to greatly enhance cybersecurity levels for various types of organizations. The research has resulted in a prototype of a web-based system that collects information about modern hardware-related vulnerabilities and provides the users with appropriate recommendations based on a specific situation.