Yazar "Yavuz, Ali Gokhan" seçeneğine göre listele

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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%.
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    Öğe
    TRConv: Multi-Platform Malware Classification via Target Regulated Convolutions
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Egitmen, Alper; Yavuz, Ali Gokhan; Yavuz, Sirma
    Malware is an important threat to digital workflow. Traditional malware modeling approaches focused on using hand-crafted features while recent approaches proved the necessity of using learning based methodologies. In this paper, we propose a novel opcode based methodology that additionally learns multiple behavioral target variables to effectively regulate and guide the static malware classification. Our methodology shows that introduction of previously extracted malware behavior-related target variables immediately improve binary malware classification performance in both Android and Windows platforms. The contributions of our methodology has been extensively validated on the AMDArgus and the MOTIF dataset. Mean classification accuracy and F1 scores suggest that our model is robust against random opcode injection attacks compared to other convolution based architectures.