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    An attack detection framework based on BERT and deep learning
    (IEEE, 2022) Seyyar, Yunus Emre; Yavuz, Ali Gökhan; Ünver, Halil Murat
    Deep 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.
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    Öğe
    Detection of Web Attacks Using the BERT Model
    (IEEE, 2022) Seyyar, Yunus Emre; Yavuz, Ali Gokhan; Unver, Halil Murat
    This paper presents a web intrusion detection system that addresses security threats with the increasing use of web applications in almost all domains, as well as the increase in attacks against web applications. Our web intrusion detection system consists of a model that can distinguish between normal and abnormal URLs. In the URL analysis phase, our model uses the BERT model of Transformers, a prominent natural language processing technique. In the classification phase, we use a CNN model, which is a popular deep learning technique. We utilize the CSIC 2010, FWAF, and HttpParams datasets for training and testing. The experimental results show that our model performs the classification of normal and abnormal requests in 0.4 ms, which is an extremely fast detection time when compared to the reported results in the literature and an accuracy of over 96%.