Intelligent Mirai Malware Detection in IoT Devices

Abstract

The advancement in recent IoT devices has led to catastrophic attacks on the devices by breaching user's privacy and exhausting the resources in organizations, which costs users and organizations time and money. One such malware which has been extremely harmful is Mirai, which has created worldwide recognition by impacting the digital world. There are several ways to mitigate Mirai, but Machine Learning-based approach has proved to be accurate and reliable in averting the malware. In this paper, a novel approach to detecting Mirai using Machine Learning Algorithm is proposed and implemented in Matlab 2018b. To evaluate the proposed approach, Mirai and Benign datasets are considered and training is performed on the dataset using Artificial Neural Network, which provides consistent results of Accuracy, Precision, Recall and F-1 score which are found to be considered accurate and reliable as the best performance was achieved with an accuracy of 92.9% and False Negative rate of 0.3, which is efficient in detecting the Mirai and is similar to the Anomaly-based Malware Detection in terms of Metrics.