1
Department of Information and Communication, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq
(heba@kecbu.uobaghdad.edu.iq)
2
Department of Information and Communication, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq
(noor.q@kecbu.uobaghdad.edu.iq)
3
Department of Information and Communication, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq
(muna@kecbu.uobaghdad.edu.iq)
4
Department of Information and Communication, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq
(zina_osama@kecbu.uobaghdad.edu.iq)
Abstract :
Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet detection for information security. For effectual recognition of botnets, the proposed model involves data pre-processing at the initial stage. Besides, the model is utilized for the identification and classification of botnets that exist in the network. In order to optimally adjust the SVM parameters, the DFA is utilized and consequently resulting in enhanced outcomes. The presented model has the ability in accomplishing improved botnet detection performance. A wide-ranging experimental analysis is performed and the results are inspected under several aspects. The experimental results indicated the efficiency of our model over existing methods.
Keywords :
Botnet detection; Machine learning; Data Classification; MSVM model; Dragonfly algorithm
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| Style | # |
|---|---|
| MLA | Heba M. Fadhil, Noor Q. Makhool, Muna M. Hummady, Zinah O. Dawood. "Machine Learning-based Information Security Model for Botnet Detection." Journal of Cybersecurity and Information Management, Vol. 9, No. 1, 2022 ,PP. 68-79 (Doi : https://doi.org/10.54216/JCIM.090106) |
| APA | Heba M. Fadhil, Noor Q. Makhool, Muna M. Hummady, Zinah O. Dawood. (2022). Machine Learning-based Information Security Model for Botnet Detection. Journal of Journal of Cybersecurity and Information Management, 9 ( 1 ), 68-79 (Doi : https://doi.org/10.54216/JCIM.090106) |
| Chicago | Heba M. Fadhil, Noor Q. Makhool, Muna M. Hummady, Zinah O. Dawood. "Machine Learning-based Information Security Model for Botnet Detection." Journal of Journal of Cybersecurity and Information Management, 9 no. 1 (2022): 68-79 (Doi : https://doi.org/10.54216/JCIM.090106) |
| Harvard | Heba M. Fadhil, Noor Q. Makhool, Muna M. Hummady, Zinah O. Dawood. (2022). Machine Learning-based Information Security Model for Botnet Detection. Journal of Journal of Cybersecurity and Information Management, 9 ( 1 ), 68-79 (Doi : https://doi.org/10.54216/JCIM.090106) |
| Vancouver | Heba M. Fadhil, Noor Q. Makhool, Muna M. Hummady, Zinah O. Dawood. Machine Learning-based Information Security Model for Botnet Detection. Journal of Journal of Cybersecurity and Information Management, (2022); 9 ( 1 ): 68-79 (Doi : https://doi.org/10.54216/JCIM.090106) |
| IEEE | Heba M. Fadhil, Noor Q. Makhool, Muna M. Hummady, Zinah O. Dawood, Machine Learning-based Information Security Model for Botnet Detection, Journal of Journal of Cybersecurity and Information Management, Vol. 9 , No. 1 , (2022) : 68-79 (Doi : https://doi.org/10.54216/JCIM.090106) |