Hossain, Muhammad IqbalAzmee, ABM.AdnanChoudhury, Pranto ProtimAlam, Md.AosafulDutta, Orko2020-03-082020-03-0820192019-12ID 16101155ID 16101062ID 16101061ID 16101022http://hdl.handle.net/10361/13837Cataloged from PDF version of thesis.Includes bibliographical references (pages 58-60).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.In this modern era of technology, securing and protecting one's data has been a major concern and needs to be focused on. Malware is a program that is designed to cause harm and malware analysis is one of the paramount focused points under the sight of cyber forensic professionals and network administrations. The degree of the harm brought about by malignant programming varies to a great extent. If this happens at home to a random person then that may lead to some loss of irrel- evant or unimportant information but for a corporate network, it can lead to loss of valuable business data. The existing research does focus on some few machine learning algorithms to detect malware and very few of them worked with Portable Executables (PE) les. However, we worked on the PE les and also for real-time computation, a client-server model was developed by using Flask to detect malware or benign. In this paper, we mainly focused on top classi cation algorithms and compare their accuracy to nd out which one is giving the best result according to the dataset and also compare among these algorithms. Top machine learning clas- si cation algorithms were used alongside neural networks such as Arti cial Neural Network, XGBoost, Support Vector Machine, Extra Tree Classi er, etc. The exper- imental result shows that XGBoost achieved the highest accuracy of 98.62 percent when compared with other approaches. Thus, to provide a better solution for this kind of anomalies, we have been interested in researching malware detection and want to contribute to building strong and protective cybersecurity.60 pages60 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Malware detectionMachine learningData protectionXGBoostSupport Vector MachineExtra Tree Classi erClient- Server ModelNeural networksMachine learningThesis