Decision Tree Based Intrusion Detection Method in the Internet of Things
Abstract
Developments in computer and network technologies have also positively affected internet technology. With the development of the Internet, the concept of IoT (Internet of Things) has been invented. Nowadays, IoT devices provide convenience in many areas, and the positive effects of IoT-based systems increase people's quality of life. People want to remotely monitor and manage smart cities, smart homes, and other platforms. However, IoT systems have many vulnerabilities and thus have become the target of attackers. Detecting such attacks and preventing security vulnerabilities will further increase the rate of use of IoT technology. In this work, an intelligent intrusion detection system (IDS) for IoT devices has been suggested. The presented intelligent IDS for IoT devices have been developed on a big attack dataset and this dataset contains 3,668,443 observations. In prior works which used this dataset, researchers worked on a binary classification problem (attacked and normal). However, this research aims to classify the attack types, hence, nine categories have been used. In order to propose a prompt responded IDS model, a fast classifier which is a decision tree (DT) has been employed. Our proposal attained 97.43% classification accuracy on this dataset using 10-fold cross-validation. This accuracy rate frankly demonstrates the classification ability of our proposed IDS model for IoT devices.
Keywords
Decision tree, Intrusion detection, Internet of Things, DDoS, DoS
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