Document Type

Article

Publication Date

12-2019

DOI

10.1109/ACCESS.2019.2960412

Publication Title

IEEE Access

Volume

7

Pages

182459-182476

Abstract

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, privileged-insider, remote hijacking, password guessing and denial of service (DoS) attacks, and malware attacks. Among these attacks, the attacks which are performed through the malware botnet (i.e., Mirai) are the malignant attacks. The existence of malware botnets leads to attacks on confidentiality, integrity, authenticity and availability of the data and other resources of the system. In presence of such attacks, the sensitive data of IoT communication may be disclosed, altered or even may not be available to the authorized users. Therefore, it becomes essential to protect the IoT/IoMT environment from malware attacks. In this review paper, we first perform the study of various types of malware attacks, and their symptoms. We also discuss some architectures of IoT environment along with their applications. Next, a taxonomy of security protocols in IoT environment is provided. Moreover, we conduct a comparative study on various existing schemes for malware detection and prevention in IoT environment. Finally, some future research challenges and directions of malware detection in IoT/IoMT environment are highlighted.

Comments

Open Access:

This work is licensed under a Creative Commons Attribution 4.0 License.

Original Publication Citation

Wazid, M., Das, A. K., Rodrigues, J., Shetty, S., & Park, Y. (2019). IoMT malware detection approaches: Analysis and research challenges. IEEE Access, 7, 182459-182476. doi:10.1109/access.2019.2960412

ORCID

0000-0002-8789-0610 (Shetty)

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