Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning

Abstract

Network security in IoT-based cloud computing can benefit greatly from the application of machine learning techniques. IoT devices introduce unique security challenges with their large-scale deployments and diverse nature. Machine learning can help address these challenges by analyzing IoT network traffic, detecting anomalies, identifying potential threats, and enhancing overall network security. The security of cloud networks is validated using binary classification to detect attacks. Random forest classifiers achieved an accuracy of 96%, while K nearest classifier had an accuracy of 93% and a precision value of 0.96. The proposed model ensures security of big data against intrusion attacks on the network. Although machine learning techniques can be powerful for protecting cloud computing networks, challenges still need to be addressed before widespread adoption. Understanding the potential and limitations of machine learning approaches to network security can
help researchers and practitioners develop more effective strategies for safeguarding their systems in an increasingly interconnected world. Network security of big data in cloud computing can be enhanced by applying machine learning techniques. Machine learning algorithms can analyze large amounts of data to detect patterns, anomalies, and potential security threats. Here are several ways machine learning can be utilized to improve network security in the context of big data and cloud computing.