Detection of Algorithmically Generated Domain Names using Ensemble Machine Learning Technique Article Swipe
S.M. Abdullahi
,
Alaa Hamid Mohammed
,
Rauf Ibrahim
,
A. S. Shamsuddeen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22624/aims/csean-smart2023p4
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22624/aims/csean-smart2023p4
Prior to now, cyber attackers use malwares with hard-coded domain names stored in the malware binaries that communicate with a command and control (C&C) servers to launch cyber-attacks on their victim computers. Malware attacks such as botnets and ransomwares are some of the most prevalent forms of these attacks. As soon as a system is infected with a malware (either a botnet or a ransomware), one of the most essential components is to establish a secured communication with the botmaster (i.e., the malware author), through a C&C server. However, with a simple reverse engineering technique, cyber security experts could detect and block these domain names, hence, denying them the ability to communicate with the C&C servers and from receiving further instructions from the botmaster. This led to cyber criminals developing the Domain Generation Algorithm (DGA) technique, which algorithmically generate thousands or more candidate’s domain names for communication with the C&C server, thereby obfuscating the domain names of these malwares and making it difficult for cyber security experts to detect or block these domain names. This paper therefore proposes an ensemble machine learning technique for the detection and classification of algorithmically generated domain names (AGDNs) leveraging the combined strength of 4 different machine learning algorithms: Naïve Bayes, SVM, Random Forest and CART. The models were trained twice, first with 4 features and thereafter with 10 features. In order to effectively utilise the result of the predictions, we used a voting-based ensemble approach, where the final classification is decided by the majority vote of the algorithms. Result of the research shows that the Naïve Bayes model performed better than all the other models with an accuracy of 97.54% when trained with 10 features and 95.99% when trained with 4 features. Keywords: WSN, DDoS, Intrusion Detection System, Random Forest, Machine Learning. Proceedings Citation Format Abdullahi, S.M., Mohammed, A., Ibrahim, R.Y. & Shamsuddeen, A. (2023): Detection of Algorithmically Generated Domain Names using Ensemble Machine Learning Technique. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 27-34. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P2.
Related Topics
Concepts
Malware
Computer science
Botnet
Domain (mathematical analysis)
Random forest
Block (permutation group theory)
Computer security
Ransomware
Artificial intelligence
Machine learning
Server
Naive Bayes classifier
Command and control
Support vector machine
Computer network
World Wide Web
Geometry
Mathematics
Mathematical analysis
The Internet
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.22624/aims/csean-smart2023p4
- https://doi.org/10.22624/aims/csean-smart2023p4
- OA Status
- bronze
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383652468
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383652468Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22624/aims/csean-smart2023p4Digital Object Identifier
- Title
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Detection of Algorithmically Generated Domain Names using Ensemble Machine Learning TechniqueWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-08Full publication date if available
- Authors
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S.M. Abdullahi, Alaa Hamid Mohammed, Rauf Ibrahim, A. S. ShamsuddeenList of authors in order
- Landing page
-
https://doi.org/10.22624/aims/csean-smart2023p4Publisher landing page
- PDF URL
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https://doi.org/10.22624/aims/csean-smart2023p4Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://doi.org/10.22624/aims/csean-smart2023p4Direct OA link when available
- Concepts
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Malware, Computer science, Botnet, Domain (mathematical analysis), Random forest, Block (permutation group theory), Computer security, Ransomware, Artificial intelligence, Machine learning, Server, Naive Bayes classifier, Command and control, Support vector machine, Computer network, World Wide Web, Geometry, Mathematics, Mathematical analysis, The Internet, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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