Abstract
The Internet of Things (IoT) has been an immediate major turning point in information and communication technology as it gives room for connection and information sharing among numerous devices. Notwithstanding, malicious code attacks have exponentially increased, with malicious code variants ranked as a major threat in resource constrained devices in IoT environment thereby making the efficient malware variants detection a serious concern for researchers in recent years. The capacity to detect malware variants is essential for protection against security breaches, data theft and other dangers. Hence with the explosion of resource constrained devices for IoT applications, it becomes very important to document existing cutting-edge techniques developed to detect malware variants in these devices. In this paper, we have investigated extensively the implementation of malware variants detection models particularly in smartphones as a case study for resource constrained devices. The paper covers the current techniques for detection of malware variants, comprehensive assessment of the techniques and recommendations for future researches.
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This research is supported by TNB Seed Fund 2019 project entitled ‘Cyber Threat Modeling for Industrial Control System and Internet of Everything’.
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Chimeleze, C.U., Jamil, N., Ismail, R., Lam, KY. (2021). A Review on Malware Variants Detection Techniques for Threat Intelligence in Resource Constrained Devices: Existing Approaches, Limitations and Future Direction. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_24
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DOI: https://doi.org/10.1007/978-981-33-6835-4_24
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