A Survey on a Security Model for the Internet of Things Environment
Bharati B. Pannayagol, Santosh L. Deshpande, Sneha Yadav · 2024
A large number of intensive applications use the Internet of Things (IoT) environment for communication. An intruder or an attacker can use the IoT environment to spread unsolicited information or can send malicious links to users. Detecting the malicious links in the IoT environment is very challenging. Defending against and detecting cyberattacks and analyzing the internet traffic are the most important work. Various machine learning algorithms are being used instead of traditional methods to detect the intruder or the attacker. Machine learning algorithms require large datasets for training and testing to provide better performance of the model. Various parameters, such as collective indication, correlation, similarity, etc., are considered when a data-driven prototype is built based on the review of the cyber traffic in IoT. This chapter presents a study on the classification of various network applications, users or hosts from the security goals of the network. In addition, this chapter provides a method for data-driven cybersecurity and its applications in the IoT environment.