ABSTRACT
Nowadays, the constant availability of Internet leaves many applications, services, and systems vulnerable to cyber-attacks such as malware and software piracy. It may compromise privacy along with their security. Software piracy such as counterfeiting, hard-disk loading, client-server overuse, and internet piracy along with malware software such as computer viruses, ransomware, scareware, and trojan horses may swipe away crucial data causing reputational as well as economic damage. In this chapter, we have proposed two deep learning approaches, that is, convolutional neural networks (CNN) and artificial neural networks (ANN), to detect malware threats in data files across the Internet of Things’ data networks. While the CNN-based model helps to detect malware through the medium of images, we can distinguish between malware and goodware using the ANN-based model from incoming data traffic.
