DNS attacks are very dangerous, and they are a problem for many small and large companies. The thesis presents how to detect and mitigate DNS attacks using new generation tools such as the PfSense Firewall, SIEM and Machine Learning models. The goals followed within the thesis are - Identify various DNS attacks and establish the most relevant ones in order to analyze them - Generate scripts to perform the attacks within an experimental network - Find a way to detect the attacks with the SIEM agents installed in the network - Use Machine Learning to detect automatically when a DNS attack is occurring
Gli attacchi DNS sono molto pericolosi e sono un problema per molte piccole e grandi aziende. La tesi presenta come rilevare e mitigare gli attacchi DNS utilizzando strumenti di nuova generazione come il Firewall PfSense, SIEM e Machine Learning. Gli obiettivi della tesi sono: - Identificare i vari attacchi DNS e stabilire quelli più rilevanti per analizzarli - Generare script per eseguire gli attacchi all'interno di una rete sperimentale - Trovare un modo per rilevare gli attacchi con gli agenti SIEM installati nella rete - Usare il Machine Learning per rilevare automaticamente quando si verifica un attacco DNS
Identification of cyber attacks using Next Generation protection tools: NGFW, NG-SIEM, AI and Machine Learning
DORIA, ALVISE
2021/2022
Abstract
DNS attacks are very dangerous, and they are a problem for many small and large companies. The thesis presents how to detect and mitigate DNS attacks using new generation tools such as the PfSense Firewall, SIEM and Machine Learning models. The goals followed within the thesis are - Identify various DNS attacks and establish the most relevant ones in order to analyze them - Generate scripts to perform the attacks within an experimental network - Find a way to detect the attacks with the SIEM agents installed in the network - Use Machine Learning to detect automatically when a DNS attack is occurringFile | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/31584