DefinePK

Abstract

This paper highlights the growing cybersecurity challenges resulting from the growing use of Internet of Things (IoT) devices. With an emphasis on the advancement of IoT security, the study employs adaptive defensive mechanisms and machine learning-based anomaly detection as proactive strategies to combat present and potential cyber threat sources. The graphic highlights the importance of having infrastructures with robust security mechanisms in place to secure connected devices and explains the Internet of Things' fast expansion. IoT security statements draw attention to the IoT's hidden vulnerabilities and threats; in these cases, state-of-the-art security measures are beneficial. Through the use of adaptive defense mechanisms and machine learning anomaly detection, the objectives center on improving IoT security. The data sources, preprocessing tasks, Random Forest, Decision Tree, SVM, and Gradient Boosting algorithms selected for anomaly detection are described in the methodology section. The integration of the adversary negotiating function and self-adaptive protection mechanisms strengthens information technology ecosystems that can simplify dynamically. In addition to providing metrics for accuracy, precision, and recall, the results and discussion section assesses the efficacy of the selected machine learning models. The most significant finding is that 89.34% more precision is achieved with gradient boosting. It has been demonstrated that the most successful model is gradient boosting. The discourse includes an explanation of the results, an acknowledgement of the limitations, and a discussion of the major difficulties encountered in conducting the study. The conclusion restates the importance of machine learning in IoT security implementation in order to build a robust system that can adjust to counter ever-evolving cyberattacks and keep up with the changing trend of securing IoT through the connected world.