Machine Learning-Based DoS Amplification Attack Detection against Constrained Application Protocol

Abstract

This paper discusses the Internet of Things (IoT) and the security challenges associated with it. IoT is a network of interconnected devices that share information. However, the low power and resources of IoT devices make them vulnerable to attacks. Using heavy protocols like HTTP for IoT devices can prove costly and using popular lightweight protocols like CoAP can invite attacks such as DoS (Denial-of-Service). While security models such as DTLS and LSPWSN can secure IoT against such attacks, they also have limitations. To overcome this problem, this paper proposes a machine learning model that detects DoS amplification attacks against CoAP with 99% accuracy. To the best of our knowledge, this research is the first to use the multi-classification process to detect and classify the different types of the DoS amplification techniques that attack CoAP client use against victim CoAP clients.

Keywords

• denial-of-service
• CoAP protocol
• IoT attacks
• datagram transport layer security (DTLS)
• amplification attacks