ABSTRACT
Artificial intelligence technology, which is used extensively in fields such as education, industry, and military defense systems today, and machine learning, which is a subbranch of this field, are important starting points for directing the health sector of the future. So, in recent years, remote monitoring systems have been developed to alleviate the burden on the healthcare field and to eliminate the need for patients to go to the hospital every time. In the development of these structures, machine learning and deep learning methods have been integrated with the Internet of Healthcare Things (IoHT), a subbranch of the Internet of Things (IoT) technology, to create systems that offer low-latency communication. Although the IoHT communication environment supports the acceleration of medical diagnosis and treatment services, it also brings some problems in the field of security. The presence of attacks such as impersonation, denial of service (DoS), and malware attacks puts the security of communication with IoT devices under threat. At this point, establishing a secure communication with IoHT devices has become a research point to be investigated. At this point, the aim of the study is to inform about current malware attacks that threaten IoHT environments and to provide comprehensive research on the latest developments in the field of deep learning to prevent and detect these attacks. In this context, various malware attacks and the effects of these attacks were examined. A discussion on IoT environment architectures, IoHT-enabling technologies, security requirements of IoT/IOHT environments, and applications of IoT systems in healthcare are also presented. Moreover, an analogy-based study on various DL-based available methods for malware detection and prevention in IoT/IoHT environment is conducted. Finally, obtained results are given; in addition to this, some future research challenges and directions of malware perception in IoT/IoMT environment are emphasized.
