Výsledky vyhledávání

Report

E-DoH: Elegantly Detecting the Depths of Open DoH Service on the Internet

Autor: Dong, Cong, Yang, Jiahai, Li, Yun, Wu, Yue, Chen, Yufan, Li, Chenglong, Jiao, Haoran, Yin, Xia, Liu, Yuling

In recent years, DNS over Encrypted (DoE) methods have been regarded as a novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS over HTTPS (DoH) provides encryption to protect data confidentiality while providing better obfusca

Externí odkaz: http://arxiv.org/abs/2403.12363

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Report

CBSeq: A Channel-level Behavior Sequence For Encrypted Malware Traffic Detection

Autor: Cui, Susu, Dong, Cong, Shen, Meng, Liu, Yuling, Jiang, Bo, Lu, Zhigang

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic

Externí odkaz: http://arxiv.org/abs/2307.09002

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Akademický článek

Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection

Autor: Dong Cong Trinh, Anh Tuan Mac, Khanh Giap Dang, Huong Thanh Nguyen, Hoc Thai Nguyen, Thanh Dang Bui

Publikováno v: AgriEngineering, Vol 6, Iss 1, Pp 302-317 (2024)

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than cur

Externí odkaz: https://doaj.org/article/ca56621185fb46e39d9990d69fd4bf09