Internet of Things Attacks Detection and Classification Using Tiered Hidden Markov Model

Abstract

Internet of Things (IoT) attacks have rapidly risen in frequency in recent years as IoT devices become more commonplace in industry, businesses, and homes. Since these devices have very basic functionality and are not designed with security in mind, they are easy targets for attacks that can steal data or gain access to the network the devices are connected to. Here we propose a tiered system of Hidden Markov Models (HMMs) for identifying these attacks and classifying them by type of attack. This system has a tree-based structure, with the main HMM being applied to the raw network data to identify attacks. This main HMM branches off into separate HMMs for each type of attack to classify the attacks according to how important the consequences of the attack are and how likely each attack is to happen.