An Attack Detection Framework Based on BERT and Deep Learning

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dc.contributor.authorSeyyar, Yunus Emre
dc.contributor.authorYavuz, Ali Gokhan
dc.contributor.authorUnver, Halil Murat
dc.date.accessioned2025-01-21T16:35:34Z
dc.date.available2025-01-21T16:35:34Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractDeep Learning (DL) and Natural Language Processing (NLP) techniques are improving and enriching with a rapid pace. Furthermore, we witness that the use of web applications is increasing in almost every direction in parallel with the related technologies. Web applications encompass a wide array of use cases utilizing personal, financial, defense, and political information (e.g., wikileaks incident). Indeed, to access and to manipulate such information are among the primary goals of attackers. Thus, vulnerability of the information targeted by adversaries is a vital problem and if such information is captured then the consequences can be devastating, which can, potentially, become national security risks in the extreme cases. In this study, as a remedy to this problem, we propose a novel model that is capable of distinguishing normal HTTP requests and anomalous HTTP requests. Our model employs NLP techniques, Bidirectional Encoder Representations from Transformers (BERT) model, and DL techniques. Our experimental results reveal that the proposed approach achieves a success rate over 99.98% and an F1 score over 98.70% in the classification of anomalous and normal requests. Furthermore, web attack detection time of our model is significantly lower (i.e., 0.4 ms) than the other approaches presented in the literature.
dc.identifier.doi10.1109/ACCESS.2022.3185748
dc.identifier.endpage68644
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85133648584
dc.identifier.scopusqualityQ1
dc.identifier.startpage68633
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3185748
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24160
dc.identifier.volume10
dc.identifier.wosWOS:000838524100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectProtocols; Bit error rate; Natural language processing; Uniform resource locators; Structured Query Language; Firewalls (computing); Deep learning; Anomalous request; BERT; deep learning; web attack; multilayer perceptron; natural language processing
dc.titleAn Attack Detection Framework Based on BERT and Deep Learning
dc.typeArticle

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