Comparative Analysis of Machine Learning Methods for Threat Analysis and Prediction in IOT Devices

The growing number of linked devices in IoT contexts has resulted in a proportional rise in security vulnerabilities. In order to ensure the security and privacy of IoT networks and devices, it is essential to have threat analysis and prediction system in place. Machine learning algorithms have emerged as a potential tool for threat analysis and prediction in the Internet of Things. Nevertheless, there exist several machine learning algorithms to choose from, each possessing its own advantages and disadvantages. This research paper presents a comparison of commonly used machine learning approaches for threat analysis and prediction in IoT scenarios. The study analyzes the merits and drawbacks of each algorithm, including their capacity to identify both familiar and unfamiliar assaults, their rates of false positives, their computing efficacy, and their prerequisites for training data. This study provides a comprehensive analysis of several machine learning techniques, such as Support Vector Machines, Artificial Neural Networks (ANN), Logistic Regression (LR), Decision Trees (DT), K-Nearest Neighbour (kNN), Random Forest (RF), Naive Bayes, and Deep Learning. The research encompasses an examination of the algorithms’ efficacy in various scenarios, along with their inherent constraints. The research concluded by providing advice for choosing the optimal machine learning technique for threat analysis and prediction in IoT contexts. The suggestions take into account the particular use case, the data that is accessible, and other pertinent variables. The report offers significant insights for enterprises seeking to enhance their IoT security stance and safeguard their devices and networks from possible attacks. 

Keywords: Threat Analysis, Machine Learning Methods, Cyber Security.