Publication:
A Review of Machine Learning Botnet Detection Techniques based on Network Traffic Log

Simple item page
dc.contributor.authorIbrahim Z.-A.en_US
dc.contributor.authorRazali R.A.en_US
dc.contributor.authorIsmail S.A.en_US
dc.contributor.authorAzhar I.H.K.en_US
dc.contributor.authorRahim F.A.en_US
dc.contributor.authorAzilan A.M.A.en_US
dc.contributor.authorid57203863738en_US
dc.contributor.authorid35146685400en_US
dc.contributor.authorid56943570600en_US
dc.contributor.authorid58109599200en_US
dc.contributor.authorid57350579500en_US
dc.contributor.authorid58109295900en_US
dc.date.accessioned2023-05-29T09:38:44Z
dc.date.available2023-05-29T09:38:44Z
dc.date.issued2022
dc.descriptionCrime; Cybersecurity; Denial-of-service attack; HIgh speed networks; Internet of things; Machine learning; Network security; Personal computing; Botmaster; Botnet detections; Botnets; Cyber-attacks; High-speed Networks; Introduction; Log.; Machine-learning; Network traffic; Related works; Botneten_US
dc.description.abstractCyber-attacks are a common issue in this modern era because of the introduction of high-speed networks and the use of new technologies like Internet of Things (IoT) devices, which fuel the rapid expansion of cyber-attack. One of the common cyber-attacks is botnet attacks. Hackers use botnet attacks to exploit newly discovered vulnerabilities in order to conduct intensive scraping, distributed denial of service (DDoS) attacks, and other large-scale cybercrime. With their adaptable and dynamic character, botnets work with a botmaster to plan their activities, modify their codes, and update the bots regularly to avoid detection. Researchers use numerous techniques to detect the botnet. However, botmasters nowadays have improved due to avoiding security in detection. As the communication can leave traces that allow researchers to detect the botnet's existence, this paper will review 15 related works on botnet detection that utilize machine learning to predict the botnet communication with the command-and-control (C&C or C2) center based on the network traffic log. This paper summarizes the related works based on the dataset, environment, botnet type, features employed, and machine learning techniques. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICOCO56118.2022.10031803
dc.identifier.epage209
dc.identifier.scopus2-s2.0-85148442424
dc.identifier.spage204
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85148442424&doi=10.1109%2fICOCO56118.2022.10031803&partnerID=40&md5=459fd12cc11b9157a3fb97fcea07d8c9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27019
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 IEEE International Conference on Computing, ICOCO 2022
dc.titleA Review of Machine Learning Botnet Detection Techniques based on Network Traffic Logen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS