TY - JOUR
T1 - Systematic Literature Review on IoT-Based Botnet Attack
AU - Ali, Ihsan
AU - Ahmed, Abdelmuttlib Ibrahim Abdalla
AU - Almogren, Ahmad
AU - Raza, Muhammad Ahsan
AU - Shah, Syed Attique
AU - Khan, Anwar
AU - Gani, Abdullah
N1 - Funding Information:
The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs and Faculty of Computer Science and Information Technology, University of Malaya, through Postgraduate Research Grant PG035-2016A.
Publisher Copyright:
© 2013 IEEE.
PY - 2020/11/24
Y1 - 2020/11/24
N2 - The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure the coverage of all important studies. This methodology was detailed and repeatable. The review outlined the existing proposed contributions, datasets utilised, network forensic methods utilised and research focus of the primary selected studies. The demographic characteristics of primary studies were also outlined.The result of this review revealed that research in this domain is gaining momentum, particularly in the last 3 years (2018-2020). Nine key contributions were also identified, with Evaluation, System, and Model being the most conducted.
AB - The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure the coverage of all important studies. This methodology was detailed and repeatable. The review outlined the existing proposed contributions, datasets utilised, network forensic methods utilised and research focus of the primary selected studies. The demographic characteristics of primary studies were also outlined.The result of this review revealed that research in this domain is gaining momentum, particularly in the last 3 years (2018-2020). Nine key contributions were also identified, with Evaluation, System, and Model being the most conducted.
KW - IoT Botnet
KW - DDoS Attack
KW - Network Security
KW - SDN
KW - Systematic Literature Review
UR - http://www.scopus.com/inward/record.url?scp=85097192612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097192612&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3039985
DO - 10.1109/ACCESS.2020.3039985
M3 - Review article
SN - 2169-3536
VL - 8
SP - 212220
EP - 212232
JO - IEEE Access
JF - IEEE Access
M1 - 9268057
ER -