Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.

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Several countries struggle to deliver cost-effective, quality healthcare services to their patients and citizens. The growth of healthcare services has rapidly increased for both health organizations and industries. The challenge of setting healthcare services remains to increase as system structure needs to satisfy several specifications and requirements of ever serious design scenarios. The industry of healthcare is progressing as the ability of producing a high value quality of service to the community, and this leads to the growth of information technology (IT) which has presented several significant solutions to healthcare services using high quality speed of network communication, mobile and digital technology which should facilitate an accessible to achieve medical services. Hence, this paper proposes and adopts a technique to proof and validate digital healthcare services which illustrates a technological method to produce health services and benefits available by using a distributed system, thus the proposed system will provide Electronic Health Records (EHR) which is using and applying several client technologies such as, web, mobile and personal computer to have a variety of services in the system. Therefore, the EHRs are managed by system modules in different criteria of health records which is shown and applied along with a typical transmission protocol to tame interconnecting between system services and modules.