TY - GEN
T1 - A Review on Malware Variants Detection Techniques for Threat Intelligence in Resource Constrained Devices
T2 - 2nd International Conference on Advances in Cyber Security, ACeS 2020
AU - Chimeleze, Collins Uchenna
AU - Jamil, Norziana
AU - Ismail, Roslan
AU - Lam, Kwok Yan
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Internet of Things
KW - Malicious software
KW - Malware variants
KW - Resource constrained devices
KW - Threat intelligence
UR - http://www.scopus.com/inward/record.url?scp=85101498137&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101498137&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-6835-4_24
DO - 10.1007/978-981-33-6835-4_24
M3 - Conference contribution
AN - SCOPUS:85101498137
SN - 9789813368347
T3 - Communications in Computer and Information Science
SP - 354
EP - 370
BT - Advances in Cyber Security - Second International Conference, ACeS 2020, Revised Selected Papers
A2 - Anbar, Mohammed
A2 - Abdullah, Nibras
A2 - Manickam, Selvakumar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 December 2020 through 9 December 2020
ER -