Malware threats and detection for industrial mobile-IoT networks

Sharmeen, Shaila, Huda, MD Shamsul, Abawajy, Jemal H, Ismail, Walaa Nagy and Hassan, Mohammad Mehedi 2018, Malware threats and detection for industrial mobile-IoT networks, IEEE access, vol. 6, pp. 15941-15957, doi: 10.1109/ACCESS.2018.2815660.

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Title Malware threats and detection for industrial mobile-IoT networks
Author(s) Sharmeen, Shaila
Huda, MD Shamsul
Abawajy, Jemal HORCID iD for Abawajy, Jemal H orcid.org/0000-0001-8962-1222
Ismail, Walaa Nagy
Hassan, Mohammad Mehedi
Journal name IEEE access
Volume number 6
Start page 15941
End page 15957
Total pages 17
Publisher Institute for Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018-03-13
ISSN 2169-3536
Keyword(s) industrial mobile IoT
threats
malware
detection method
machine learning
science & technology
technology
computer science, information systems
engineering, electrical & electronic
telecommunications
computer science
engineering
security
Summary Industrial IoT networks deploy heterogeneous IoT devices to meet a wide range of user requirements. These devices are usually pooled from private or public IoT cloud providers. A significant number of IoT cloud providers integrate smartphones to overcome the latency of IoT devices and low computational power problems. However, the integration of mobile devices with industrial IoT networks exposes the IoT devices to significant malware threats. Mobile malware is the highest threat to the security of IoT data, user's personal information, identity, and corporate/financial information. This paper analyzes the efforts regarding malware threats aimed at the devices deployed in industrial mobile-IoT networks and related detection techniques. We considered static, dynamic, and hybrid detection analysis. In this performance analysis, we compared static, dynamic, and hybrid analyses on the basis of data set, feature extraction techniques, feature selection techniques, detection methods, and the accuracy achieved by these methods. Therefore, we identify suspicious API calls, system calls, and the permissions that are extracted and selected as features to detect mobile malware. This will assist application developers in the safe use of APIs when developing applications for industrial IoT networks.
Language eng
DOI 10.1109/ACCESS.2018.2815660
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109526

Document type: Journal Article
Collections: School of Information Technology
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