Optimal Graph Convolutional Neural Network-Based Ransomware Detection for Cybersecurity in IoT Environment

The fast development of the Internet of Things (IoT) and widespread utilization in a large number of areas, such as vehicle IoT, industrial control, healthcare, and smart homes, has made IoT security increasingly prominent. Ransomware is a type of malware which encrypts the victim's records and demands a ransom payment for restoring access. The effective detection of ransomware attacks highly depends on how its traits are discovered and how precisely its activities are understood. In this article, we propose an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for cybersecurity in an IoT environment. The OGCNN-RWD technique involves learning enthusiasm for teaching learning-based optimization (LETLBO) algorithms for the feature subset selection process. For ransomware classification, the GCNN model is used in this study, and its hyperparameters can be optimally chosen by the harmony search algorithm (HSA). For exhibiting the greater performance of the OGCNN-RWD approach, a series of simulations were made on the ransomware database. The simulation result portrays the betterment of the OGCNN-RWD system over other existing techniques with an accuracy of 99.64%.

Automated summary

The rapid development and widespread utilization of the Internet of Things (IoT) have brought cybersecurity to the forefront. This article introduces an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for IoT security. By utilizing learning enthusiasm for feature subset selection and the GCNN model for ransomware classification, the OGCNN-RWD system outperforms other existing techniques with an accuracy of 99.64% according to simulation experiments on a ransomware database.