TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Two Stage Deep Learning Based Stacked Ensemble Model for Web Application Security |
| 영문제목(English Title) |
Two Stage Deep Learning Based Stacked Ensemble Model for Web Application Security |
| 저자(Author) |
Waheeb Ahmed
Di Wu
Daniel Mukathie
Mehmet Sevri
Hacer Karacan
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| 원문수록처(Citation) |
VOL 16 NO. 02 PP. 0632 ~ 0657 (2022. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients’ requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZIHTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.
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| 키워드(Keyword) |
VANET
Trust management
Blockchain
Bogus message attack
Privacy
Anomaly detection
deep learning
nsemble learning
web application firewall
web security
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| 파일첨부 |
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