Malware Detection Using Machine Learning Classifiers

Camara, Ahmed; Salam, Aanmar Abdou; Abdallah, Mefire

dc.contributor.author	Camara, Ahmed
dc.contributor.author	Salam, Aanmar Abdou
dc.contributor.author	Abdallah, Mefire
dc.date.accessioned	2023-04-03T08:04:44Z
dc.date.available	2023-04-03T08:04:44Z
dc.date.issued	2022-05-31
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dc.identifier.uri	http://hdl.handle.net/123456789/1807
dc.description	Supervised by Mr. Shohel Ahmed, Assistant Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	With the growth of technology, and the exponential amount of data that is being generated, the main challenge is to figure out how to protect this data from unauthorized access. Over the last couple of years, researchers have struggled to come up with a best solution that would handle this problem. The signature-based detection was the standard method used to detect malware. Regrettably, traditional technologies are no longer capable of providing adequate protection. In this work, we proposed a protection system where we trained different models in machine learning to learn from malicious and benign files to allow future prediction. We trained three classifiers in this work, Random Forest, Decision Tree, and KNearest Neighbors on the data. Random Forest gives the best result with an FPR value of 0.0208 and an accuracy of 98%.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Malware, Machine Learning, Random Forest, Decision Tree, K-NN,Sampling	en_US
dc.title	Malware Detection Using Machine Learning Classifiers	en_US
dc.type	Thesis	en_US