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Malware Detection in Executable files using XGBoost Algorithm

Pant, Yashvardhan (2022) Malware Detection in Executable files using XGBoost Algorithm. Masters thesis, Dublin, National College of Ireland.

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Keeping up with the fast growth of modern technology has led to an increase in malware and harmful activities, which hackers are using to their advantage in stealing personal information and login passwords. Rising numbers of malicious software regularly attacking online systems now pose a significant risk. Due to the rapid growth of malware, manual heuristic inspection of samples is no longer regarded a viable method of analysis. The use of machine learning for automated, behavior-based malware detection and static approach with PE- header based malware detection information is thus seen as a powerful approach. In this study, examination of many Machine Learning Algorithms (such as XGBoost, KNN, and Random Forest) that may be used to detect malware by analyzing a large dataset. Here, measurement of how well things is doing utilizing a confusion matrix and a focus on Detection. XGBoost has the highest accuracy and precision of any method, with 98.292% and 99.15%, without feature extraction and 98.33% and 99.01% with feature extraction respectively.

Item Type: Thesis (Masters)
Khan, Imran
Uncontrolled Keywords: Malware Detection; Machine Learning; Deep learning; Security; Algorithm; Dataset; XGBoost
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 04 May 2023 15:23
Last Modified: 04 May 2023 15:23

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