Abstract
Botnets are rising as a platform for many unlawful cyber activities such as Distributed Denial of Service (DDoS) attacks, malware dissemination, phishing, click fraud, and so on. As of late, detecting botnet has been an intriguing research topic in relation to cybercrime analysis and cyber-threat prevention. This paper is an analysis of publications related to botnet detection techniques. We analyse 194 botnet related papers published between 2009 and 2018 in the ISI Web of Science database. Seven (7) criteria have been used for this analysis to detect highly-cited articles, most impactful journals, current research areas, most active researchers and institutions in the field. It was noted that the average number of publications related to botnet detection have been reduced recently, which could be because of overwhelming existing literature in the field. Asia is the most active and most productive continent in botnet research and computer science is the research area with most publications related to botnet detection as expected.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
S. Homayoun, M. Ahmadzadeh, S. Hashemi, A. Dehghantanha, and R. Khayami, “BoTShark: A Deep Learning Approach for Botnet Traffic Detection,” 2018, pp. 137–153.
M. Hopkins and A. Dehghantanha, “Exploit Kits: The production line of the Cybercrime economy?,” in 2015 2nd International Conference on Information Security and Cyber Forensics, InfoSec 2015, 2016.
M. Damshenas, A. Dehghantanha, K.-K. R. Choo, and R. Mahmud, “M0Droid: An Android Behavioral-Based Malware Detection Model,” J. Inf. Priv. Secur., Sep. 2015.
A. Azmoodeh, A. Dehghantanha, M. Conti, and K.-K. R. Choo, “Detecting crypto-ransomware in {IoT} networks based on energy consumption footprint,” J. Ambient Intell. Humaniz. Comput., Aug. 2017.
A. Shalaginov, S. Banin, A. Dehghantanha, and K. Franke, Machine learning aided static malware analysis: A survey and tutorial, vol. 70. 2018.
O. Osanaiye, H. Cai, K.-K. R. Choo, A. Dehghantanha, Z. Xu, and M. Dlodlo, “Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing,” Eurasip J. Wirel. Commun. Netw., vol. 2016, no. 1, 2016.
J. Baldwin and A. Dehghantanha, Leveraging support vector machine for opcode density based detection of crypto-ransomware, vol. 70. 2018.
D. Kiwia, A. Dehghantanha, K. K. R. Choo, and J. Slaughter, “A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence,” J. Comput. Sci., 2017.
D. Zhao et al., “Botnet detection based on traffic behavior analysis and flow intervals,” Comput. Secur., vol. 39, no. PARTA, pp. 2–16, 2013.
H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting,” Futur. Gener. Comput. Syst., 2018.
K. Singh, S. C. Guntuku, A. Thakur, and C. Hota, “Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests,” Inf. Sci. (Ny)., vol. 278, pp. 488–497, 2014.
O. M. K. Alhawi, J. Baldwin, and A. Dehghantanha, Leveraging machine learning techniques for windows ransomware network traffic detection, vol. 70. 2018.
H. H. Pajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “Intelligent OS X malware threat detection with code inspection,” J. Comput. Virol. Hacking Tech., 2017.
A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning,” IEEE Trans. Sustain. Comput., pp. 1–1, 2018.
S. Homayoun, A. Dehghantanha, M. Ahmadzadeh, S. Hashemi, and R. Khayami, “Know Abnormal, Find Evil: Frequent Pattern Mining for Ransomware Threat Hunting and Intelligence,” IEEE Trans. Emerg. Top. Comput., pp. 1–1, 2017.
H. Haughey, G. Epiphaniou, H. Al-Khateeb, and A. Dehghantanha, “Adaptive Traffic Fingerprinting for Darknet Threat Intelligence,” 2018, pp. 193–217.
M. Conti, A. Dehghantanha, K. Franke, and S. Watson, “Internet of Things security and forensics: Challenges and opportunities,” Futur. Gener. Comput. Syst., vol. 78, pp. 544–546, Jan. 2018.
H. Haddad Pajouh, R. Javidan, R. Khayami, D. Ali, and K.-K. R. Choo, “A Two-layer Dimension Reduction and Two-tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks,” IEEE Trans. Emerg. Top. Comput., pp. 1–1, 2016.
N. Milosevic, A. Dehghantanha, and K.-K. R. Choo, “Machine learning aided Android malware classification,” Comput. Electr. Eng., vol. 61, 2017.
S. Ranjan, J. Robinson, and F. Chen, “Machine Learning Based Botnet Detection Using Real-Time Connectivity Graph Based Traffic Features,” 2015.
S. Ranjan and F. Chen, “Machine Learning Based Botnet Detection With Dynamic Adaptation,” 2006.
J. Baldwin, O. M. K. Alhawi, S. Shaughnessy, A. Akinbi, and A. Dehghantanha, Emerging from the cloud: A bibliometric analysis of cloud forensics studies, vol. 70. 2018.
J. Gill, I. Okere, H. HaddadPajouh, and A. Dehghantanha, Mobile forensics: A bibliometric analysis, vol. 70. 2018.
I. Ghafir, V. Prenosil, and M. Hammoudeh, “Botnet Command and Control Traffic Detection Challenges : A Correlation-based Solution,” no. April, pp. 1–5, 2017.
G. Kirubavathi and R. Anitha, “Botnet detection via mining of traffic flow characteristics,” Comput. Electr. Eng., vol. 50, pp. 91–101, 2016.
J. A. Jerkins, “Motivating a market or regulatory solution to IoT insecurity with the Mirai botnet code,” 2017 IEEE 7th Annu. Comput. Commun. Work. Conf. CCWC 2017, 2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Amina, S., Vera, R., Dargahi, T., Dehghantanha, A. (2019). A Bibliometric Analysis of Botnet Detection Techniques. In: Dehghantanha, A., Choo, KK. (eds) Handbook of Big Data and IoT Security. Springer, Cham. https://doi.org/10.1007/978-3-030-10543-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-10543-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10542-6
Online ISBN: 978-3-030-10543-3
eBook Packages: Computer ScienceComputer Science (R0)