Assessing and Categorizing Security Threats in the IoT Ecosystem: Machine learning based Comprehensive Examination of Vulnerabilities

The Internet of Things ecosystem has experienced skyrocketing expansion in terms of both devices and their application, turning it into an ultimate innovational tool in literally all spheres of personal life and industrial production. An integral aspect of such rapid and ultimate growth has also become the security challenges faced by IoT devices, which present a prime target for malefactors. Thus, to assess these threats and ways to counter them, machine learning algorithms like DBSCAN, One-Class SVM, and Ensemble Methods are utilized. This research is based on the use of various datasets and performance of multiple experiment trials. The results confirm the moderate to high quality of clustering of DBSCAN. The Silhouette Score varies between 0.72 and 0.81. One-Class SVM, in turn, shows high precision, with scores from 0.79 to 0.89. Finally, the Ensemble Methods demonstrate high accuracy, with the values of 0.87-0.94. Thus, the research highlights the extent to which machine learning may help to identify and categorize security threats within the context of IoT. Implications of research could be the way to improve IoT security practices; machine learning might be successful in this regard. In addition, future areas of research could focus on algorithms in real-time to optimize threat identification and response; alternatively, new ideas could be developed to account for new security problems by analyzing IoT activity.