Document Details

Document Type : Thesis 
Document Title :
Thesis Title: ENHANCING CYBER THREAT HUNTING TECHNIQUE FOR PREVENTING RANSOMWARE ATTACKS: A PROACTIVE APPROACH
تحسين تقنية اقتناص التهديدات السبرانية لمنع هجمات فيروس الفدية : نهج استباقي
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : Software that contains destructive commands intended to damage user data and systems is known as malware. The malware objective is to take over the system without authorization, view important data, or even corrupt it. Over the years, several malware forms have threatened systems and data. Ransomware is among the most damaging malware since it results in significant losses. In order to get a ransom, ransomware is software that locks the victim's machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks. For proactive forecasting, Artificial Intelligence (AI) techniques are used. Traditional machine learning / deep learning (ML/DL) techniques, however, take a lot of time and decrease the accuracy and latency performance of network monitoring. In this study, we will utilize three algorithms of the Hoeffding trees classifier as one of the stream data mining classification techniques to detect and prevent ransomware attacks. Consequently, when ransomware attacks are identified as early as possible, cyber threat hunting technique performance is improved. In conclusion, the 99.41 percent classification accuracy was the highest result achieved by the extremely fast decision trees (EFDT) algorithm in 66 ms. 
Supervisor : Dr. Omar Abdullah Batarfi 
Thesis Type : Doctorate Thesis 
Publishing Year : 1445 AH
2023 AD 
Added Date : Sunday, October 29, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
إبراهيم محمد باعبادBaabbad, Ibrahim MohammedResearcherDoctorate 

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 49454.pdf pdf 

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