AI-empowered malware detection system for industrial internet of things

被引：6
作者：
Smmarwar S.K. [1 ]
Gupta G.P. [1 ]
Kumar S. [1 ]
机构：
[1] Department of Information Technology, National Institute of Technology, Raipur
来源：
Computers and Electrical Engineering | 2023年 / 108卷
关键词：
CNN-LSTM; Cyber-attacks; Deep learning; Double-density discrete wavelet transform; Industrial internet of things; Malware detection;
D O I：
10.1016/j.compeleceng.2023.108731
中图分类号：
学科分类号：
摘要：
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. © 2023 Elsevier Ltd
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