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Iterative methods and parallel computation for power systems

Autor: Daǧ, Hasan.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1996.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 136-152).

Externí odkaz: http://catalog.hathitrust.org/api/volumes/oclc/35031860.html

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Phishing Website Detection through Multi-Model Analysis of HTML Content

Autor: Çolhak, Furkan, Ecevit, Mert İlhan, Uçar, Bilal Emir, Creutzburg, Reiner, Dağ, Hasan

The way we communicate and work has changed significantly with the rise of the Internet. While it has opened up new opportunities, it has also brought about an increase in cyber threats. One common and serious threat is phishing, where cybercriminals

Externí odkaz: http://arxiv.org/abs/2401.04820

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SecureReg: Combining NLP and MLP for Enhanced Detection of Malicious Domain Name Registrations

Autor: Çolhak, Furkan, Ecevit, Mert İlhan, Dağ, Hasan, Creutzburg, Reiner

The escalating landscape of cyber threats, characterized by the registration of thousands of new domains daily for large-scale Internet attacks such as spam, phishing, and drive-by downloads, underscores the imperative for innovative detection method

Externí odkaz: http://arxiv.org/abs/2401.03196

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An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

Autor: Demirkıran, Ferhat, Çayır, Aykut, Ünal, Uğur, Dağ, Hasan

Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to take precautions against malwa

Externí odkaz: http://arxiv.org/abs/2112.13236

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Benchmark Static API Call Datasets for Malware Family Classification

Autor: Düzgün, Berkant, Çayır, Aykut, Demirkıran, Ferhat, Kahya, Ceyda Nur, Gençaydın, Buket, Dağ, Hasan

Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security experts to detect malware an

Externí odkaz: http://arxiv.org/abs/2111.15205

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Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

Autor: Çayır, Aykut, Ünal, Uğur, Dağ, Hasan

Behavior of a malware varies with respect to malware types. Therefore,knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accurac

Externí odkaz: http://arxiv.org/abs/1912.10836

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