AI-empowered malware detection system for industrial internet of things

Abstract

• AI-enabled Malware Detection System for Industrial Internet of Things is presented. • Double Density Discrete Wavelet Transform method is used for learning the features. • Hybrid CNN-LSTM model is used for the identification of the malware . • Higher classification accuracy is achieved using the proposed method. 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.