A dynamic malware detection in cloud platform

Cloud Computing platform is the practices of remote manage network resources such as storage, application hosted on the internet rather than physical server or personal computer. Hence cloud computing not only provides high availability on elastic resources, scalable and cost efficient. This is why...

全面介紹

Saved in:

書目詳細資料
主要作者:	Lee, Nani Yer Fui
格式:	Thesis
語言:	English
出版:	2019
主題:	Malware (Computer software) Cloud computing
在線閱讀:	http://psasir.upm.edu.my/id/eprint/83868/1/FSKTM%202019%2041%20-%20IR.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

id	my-upm-ir.83868
record_format	uketd_dc
spelling	my-upm-ir.838682020-10-26T03:48:57Z A dynamic malware detection in cloud platform 2019-06 Lee, Nani Yer Fui Cloud Computing platform is the practices of remote manage network resources such as storage, application hosted on the internet rather than physical server or personal computer. Hence cloud computing not only provides high availability on elastic resources, scalable and cost efficient. This is why this this platform is widely used in information technology (IT) to support technology infrastructure and services. However, due to the complexity environment and scalability of services, one of a highest security issue is malware attacks; where some of the antivirus scanner unable to detect metamorphic malware or encrypted malware where these kind of malware able to bypass some traditional protection solution. This is why a high recognition rate and a good precision detection are important to eliminate high false positive rate. Machine learning (ML) classifiers are critical role in the artificial intelligent-system such as medical assistance detect whether the cell is cancerous or benign or to convert the spoken audio file into a text file. However machine learning will require learn from high amplitude of input data; classify then only able to generate a reliable model with high detection rate. The objective in this work is to study and performs detection based on dynamic malware analysis and classification is through WEKA classifier and Random Forest Jupyter Notebook. In this work we assess five classifiers, for instance the Random Forest in WEKA, Decision Tree (J48) in WEKA and Bayes Network (BN) in WEKA tool, and Random Forest in Jupyter Notebook comprised 9600 malware dataset obtained from Kaggle to exhibit the model’s effectiveness, out of which additional 600 are new malware dataset, whereby previous solution consist 9000 malware dataset. Malware (Computer software) Cloud computing 2019-06 Thesis http://psasir.upm.edu.my/id/eprint/83868/ http://psasir.upm.edu.my/id/eprint/83868/1/FSKTM%202019%2041%20-%20IR.pdf text en public masters Universiti Putra Malaysia Malware (Computer software) Cloud computing Asmawi, Aziah
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Asmawi, Aziah
topic	Malware (Computer software) Cloud computing
spellingShingle	Malware (Computer software) Cloud computing Lee, Nani Yer Fui A dynamic malware detection in cloud platform
description	Cloud Computing platform is the practices of remote manage network resources such as storage, application hosted on the internet rather than physical server or personal computer. Hence cloud computing not only provides high availability on elastic resources, scalable and cost efficient. This is why this this platform is widely used in information technology (IT) to support technology infrastructure and services. However, due to the complexity environment and scalability of services, one of a highest security issue is malware attacks; where some of the antivirus scanner unable to detect metamorphic malware or encrypted malware where these kind of malware able to bypass some traditional protection solution. This is why a high recognition rate and a good precision detection are important to eliminate high false positive rate. Machine learning (ML) classifiers are critical role in the artificial intelligent-system such as medical assistance detect whether the cell is cancerous or benign or to convert the spoken audio file into a text file. However machine learning will require learn from high amplitude of input data; classify then only able to generate a reliable model with high detection rate. The objective in this work is to study and performs detection based on dynamic malware analysis and classification is through WEKA classifier and Random Forest Jupyter Notebook. In this work we assess five classifiers, for instance the Random Forest in WEKA, Decision Tree (J48) in WEKA and Bayes Network (BN) in WEKA tool, and Random Forest in Jupyter Notebook comprised 9600 malware dataset obtained from Kaggle to exhibit the model’s effectiveness, out of which additional 600 are new malware dataset, whereby previous solution consist 9000 malware dataset.
format	Thesis
qualification_level	Master's degree
author	Lee, Nani Yer Fui
author_facet	Lee, Nani Yer Fui
author_sort	Lee, Nani Yer Fui
title	A dynamic malware detection in cloud platform
title_short	A dynamic malware detection in cloud platform
title_full	A dynamic malware detection in cloud platform
title_fullStr	A dynamic malware detection in cloud platform
title_full_unstemmed	A dynamic malware detection in cloud platform
title_sort	dynamic malware detection in cloud platform
granting_institution	Universiti Putra Malaysia
publishDate	2019
url	http://psasir.upm.edu.my/id/eprint/83868/1/FSKTM%202019%2041%20-%20IR.pdf
_version_	1747813423700246528

A dynamic malware detection in cloud platform

相似書籍