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AuthorAlatawi, Mohammed Naif
AuthorAlsubaie, Najah
AuthorUllah Khan, Habib
AuthorSadad, Tariq
AuthorAlwageed, Hathal Salamah
AuthorAli, Shaukat
AuthorZada, Islam
Available date2023-07-23T11:19:39Z
Publication Date2023-02-28
Publication NameSecurity and Communication Networks
Identifierhttp://dx.doi.org/10.1155/2023/8048311
CitationAlatawi, M. N., Alsubaie, N., Ullah Khan, H., Sadad, T., Alwageed, H. S., Ali, S., & Zada, I. (2023). Cyber Security against Intrusion Detection Using Ensemble-Based Approaches. Security and Communication Networks, 2023.
ISSN1939-0114
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149150226&origin=inward
URIhttp://hdl.handle.net/10576/46148
AbstractThe attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.
Languageen
PublisherHindawi
SubjectIntrusion detection
Privacy and security
swarm intelligence
TitleCyber Security against Intrusion Detection Using Ensemble-Based Approaches
TypeArticle
Volume Number2023
ESSN1939-0122
dc.accessType Open Access


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