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pro vyhledávání: '"domain names"'
The persistent threat posed by malicious domain names in cyber-attacks underscores the urgent need for effective detection mechanisms. Traditional machine learning methods, while capable of identifying such domains, often suffer from high false posit
Externí odkaz:
http://arxiv.org/abs/2410.02096
Autor:
Nam, Ihyun, Wan, Gerry
In this paper, we introduce Clid, a Transport Layer Security (TLS) client identification tool based on unsupervised learning on domain names in the server name indication (SNI) field. Clid aims to provide some information on a wide range of clients,
Externí odkaz:
http://arxiv.org/abs/2410.02040
In cybersecurity, allow lists play a crucial role in distinguishing safe websites from potential threats. Conventional methods for compiling allow lists, focusing heavily on website popularity, often overlook infrequently visited legitimate domains.
Externí odkaz:
http://arxiv.org/abs/2410.02097
Autor:
Ayoub, Ibrahim, Lenders, Martine S., Ampeau, Benoît, Balakrichenan, Sandoche, Khawam, Kinda, Schmidt, Thomas C., Wählisch, Matthias
In this paper, we investigate the domain names of servers on the Internet that are accessed by IoT devices performing machine-to-machine communications. Using machine learning, we classify between them and domain names of servers contacted by other t
Externí odkaz:
http://arxiv.org/abs/2404.15068
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Domain squatting is a technique used by attackers to create domain names for phishing sites. In recent phishing attempts, we have observed many domain names that use multiple techniques to evade existing methods for domain squatting. These domain nam
Externí odkaz:
http://arxiv.org/abs/2310.11763