Analysis of malware prediction based on infection rate using machine learning techniques
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/14056 |
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10361-140562022-01-26T10:21:47Z Analysis of malware prediction based on infection rate using machine learning techniques Zawad, Safir Mansur, Raiyan Evan, Nahian Asad, Ashub Bin Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Human learning techniques Malware prediction Neural Networks This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 22-23). In this modern, technological age, the internet has been adopted by the masses. And with it, the danger of malicious attacks by cybercriminals have increased. These attacks are done via Malware, and have resulted in billions of dollars of financial damage. Which is why prevention of malware attacks has become an essential part of the battle against cybercrime. In recent years, Machine Learning has become an important tool in the field of Malware Detection, which is the first step towards removing malware from infected devices. In this thesis, we are applying machine learning algorithms to predict the malware infection rates of computers based on its features. We are using supervised machine learning algorithms and gradient boosting algorithms, such as LightGBM, Neural Networks, and Decision Tree Learning. We have collected a publicly available dataset, which was divided into two parts, one being the training set, and the other will be the testing set. After conducting four different experiments using the aforementioned algorithms, it has been discovered that LightGBM is the best model with an AUC Score of 0.73926. Safir Zawad Raiyan Mansur Nahian Evan Ashub Bin Asad B. Computer Science 2020-10-12T05:52:02Z 2020-10-12T05:52:02Z 2019 2019-12 Thesis ID: 19241038 ID: 19241037 ID: 19241036 ID: 15301062 http://hdl.handle.net/10361/14056 en_US Brac 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. 28 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Human learning techniques Malware prediction Neural Networks |
| spellingShingle |
Human learning techniques Malware prediction Neural Networks Zawad, Safir Mansur, Raiyan Evan, Nahian Asad, Ashub Bin Analysis of malware prediction based on infection rate using machine learning techniques |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Zawad, Safir Mansur, Raiyan Evan, Nahian Asad, Ashub Bin |
| format |
Thesis |
| author |
Zawad, Safir Mansur, Raiyan Evan, Nahian Asad, Ashub Bin |
| author_sort |
Zawad, Safir |
| title |
Analysis of malware prediction based on infection rate using machine learning techniques |
| title_short |
Analysis of malware prediction based on infection rate using machine learning techniques |
| title_full |
Analysis of malware prediction based on infection rate using machine learning techniques |
| title_fullStr |
Analysis of malware prediction based on infection rate using machine learning techniques |
| title_full_unstemmed |
Analysis of malware prediction based on infection rate using machine learning techniques |
| title_sort |
analysis of malware prediction based on infection rate using machine learning techniques |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/14056 |
| work_keys_str_mv |
AT zawadsafir analysisofmalwarepredictionbasedoninfectionrateusingmachinelearningtechniques AT mansurraiyan analysisofmalwarepredictionbasedoninfectionrateusingmachinelearningtechniques AT evannahian analysisofmalwarepredictionbasedoninfectionrateusingmachinelearningtechniques AT asadashubbin analysisofmalwarepredictionbasedoninfectionrateusingmachinelearningtechniques |
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