AI-empowered malware detection system for industrial internet of things

Abstract

With the significant growth in Industrial Internet of Things (IIoT) technologies, various IIoT-based applications have emerged in the last decade. In recent years, various malware-based cyber-attacks have been reported on IIoT-based systems. Thus, this research work outlines the design of an efficient Artificial Intelligence (AI)-empowered zero-day malware detection system for IIoT. In this paper, a hybrid deep learning-based malware detection framework is proposed in which a Double-Density Discrete Wavelet Transform (D3WT) is used for feature extraction and a hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is used for identification and classification of malware. The assessment of the proposed framework is evaluated using three datasets such as IoT malware, Microsoft BIG-2015 and Malimg dataset. Experimental results show that the proposed model achieved 99.98% accuracy on the IoT malware, 96.97% accuracy on the Microsoft BIG-2015 and 99.96% accuracy with the Malimg dataset.