2021 BRN Discussion, page-18708

https://www.academia.edu/9440090/Ev...us_Domains?email_work_card=abstract-read-more


Firewall for Detecting Algorithmically Generated Malicious Domains


Kostantinos Demertzis

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26 Pages
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Information Systems,
Computer Science,
Artificial Intelligence,
Information Technology,
Information Security
...more ▾
Domain Generation Algorithm (DGA) has evolved as one of the most dangerous and “undetectable” digital security deception methods. The complexity of this approach (combined with the intricate function of the fast-flux “botnet” networks) is the cause of an extremely risky threat which is hard to trace. In most of the cases it should be faced as a zero-day vulnerability. This kind of combined attacks is responsible for malware distribution and for the infection of Information Systems. Moreover it is related to illegal actions, like money mule recruitment sites, phishing websites, illicit online pharmacies, extreme or illegal adult content sites, malicious browser exploit sites and web traps for distributing virus. Traditional digital security mechanisms phase such vulnerabilities in a conventional manner, they create often false alarms and they fail to forecast them. This paper proposes an innovative fast and accurate evolving Smart URL Filter (eSURLF) in a Zone-based Policy Firewall (ZFW) which uses evolving Spiking Neural Networks (eSNN) for detecting algorithmically generated malicious domains names