Intelligent Mirai Malware Detection for IoT Nodes

The advancement in recent IoT devices has led to catastrophic attacks on the resulting breaches user privacy and exhausting resources of various organizations, so that users organizations expend increased time money. One such harmful malware is Mirai, which created worldwide recognition by impacting digital world. There are several ways detect but Machine Learning approach proved be accurate reliable detecting malware. In this research, a novel-based Mirai using Algorithm proposed implemented Matlab Python. To evaluate approaches, Benign datasets considered training performed dataset comprised Training set, Cross-Validation set Test Artificial Neural Network (ANN) consisting neurons hidden layer, provides consistent accuracy, precision, recall F-1 score. an number layers chosen avoid problem Overfitting. This research comparative analysis between ANN Random Forest models formed merging benign detection pertaining seven devices. used “N-BaIoT” dataset, represents data features infected Malware. results found as best performance was achieved with accuracy 92.8% False Negative rate 0.3% score 0.99. expected outcomes project, include major findings towards cost-effective solutions Malware strains.