Predicting Malicious Software in IoT Environment Based on Machine Learning and Data Mining Techniques

Abstract

The Internet of Things (IoT) enable the IoT to sense and respond using the power of computing to autonomously come up with the best solutions for any industry today. However, Internet of Things have vulnerabilities since it can be hacked by cybercriminals. The cybercriminals know where the IoT vulnerabilities are, such as unsecured update mechanisms and malware (Malicious Software) to attack the IoT devices. The recently posted IoT-23 dataset based on several IoT devices such as Philips Hue, Amazon Echo devices and Somfy door lock were used for machine learning classification algorithms and data mining techniques with training and testing for predictive modelling of a variety of malware attacks like Distributed Denial of Service (DDoS), Command and Control (C&C) and various IoT botnets like Mirai and Okiru. This paper aims to develop predictive modeling that will predict malicious software to protect IoT and reduce vulnerabilities by using machine learning and data mining techniques. We collected, analyzed and processed benign and several of malicious software in IoT network traffic. Malware prediction is crucial in maintaining IoT devices’ safety and security from cybercriminals’ activities. Furthermore, the Principal Component Analysis (PCA) method was applied to determine the important features of IoT-23. In addition, this study compared with previous studies that used the IoT-23 dataset in terms of accuracy rate and other metrics. Experiments show that Random Forest (RF) classifier achieved the predictive model produced classification accuracy 0.9714% as well as predict 8754 samples with various types of malware and obtained 0.9644% of Area Under Curve (AUC) which outperforms several bassline machine learning classification models.