Improving malicious detection rate for Facebook application in OSN platform

Online social networks (OSNs) have become the new vector for cybercrime, and hackers are finding new ways to propagate spam and malware on these platforms, which we refer to as social malware. As we show here, social malware cannot be identified with existing security mechanisms (e.g., URL blacklist...

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Main Author:	Angamuthu, Laavanya
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Malware (Computer software) - Prevention Online social networks - Management - Security measures
Online Access:	http://psasir.upm.edu.my/id/eprint/68968/1/FSKTM%202018%2041%20IR.pdf
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id	my-upm-ir.68968
record_format	uketd_dc
spelling	my-upm-ir.689682019-06-19T03:23:05Z Improving malicious detection rate for Facebook application in OSN platform 2018-06 Angamuthu, Laavanya Online social networks (OSNs) have become the new vector for cybercrime, and hackers are finding new ways to propagate spam and malware on these platforms, which we refer to as social malware. As we show here, social malware cannot be identified with existing security mechanisms (e.g., URL blacklists), because it exploits different weaknesses and often has different intentions. In this dissertation, we show that social malware is prevalent in Facebook, the largest OSN to date with more then a billion users and develop an efficient and scalable social malware detection system that takes advantage of the social context of posts. We deploy this detection system to detect malicious in order protect Facebook users from social malware. We find that our detection method is both accurate and efficient. Furthermore, we show that, social malware significantly differs from traditional email spam or web-based malware. One of the major factors for enabling social malware is malicious third-party apps. We show that such malicious apps are also widespread in Facebook. Therefore, to identify malicious apps, we ask the question: given a Facebook application, can we determine if it is malicious? Our key contribution in this part is in developing malware detection in Facebook third party application by using Naïve Bayes algorithm technique .We identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Then, leveraging these distinguishing features, we show that can detect malicious apps with 99.5% accuracy, with no false positives and a low false negative rate (4.1%). Finally, we explore the ecosystem of malicious Facebook apps. We identify mechanisms these apps use to propagate and find that many apps collude and support each other. Malware (Computer software) - Prevention Online social networks - Management - Security measures 2018-06 Thesis http://psasir.upm.edu.my/id/eprint/68968/ http://psasir.upm.edu.my/id/eprint/68968/1/FSKTM%202018%2041%20IR.pdf text en public masters Universiti Putra Malaysia Malware (Computer software) - Prevention Online social networks - Management - Security measures
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Malware (Computer software) - Prevention Online social networks - Management - Security measures
spellingShingle	Malware (Computer software) - Prevention Online social networks - Management - Security measures Angamuthu, Laavanya Improving malicious detection rate for Facebook application in OSN platform
description	Online social networks (OSNs) have become the new vector for cybercrime, and hackers are finding new ways to propagate spam and malware on these platforms, which we refer to as social malware. As we show here, social malware cannot be identified with existing security mechanisms (e.g., URL blacklists), because it exploits different weaknesses and often has different intentions. In this dissertation, we show that social malware is prevalent in Facebook, the largest OSN to date with more then a billion users and develop an efficient and scalable social malware detection system that takes advantage of the social context of posts. We deploy this detection system to detect malicious in order protect Facebook users from social malware. We find that our detection method is both accurate and efficient. Furthermore, we show that, social malware significantly differs from traditional email spam or web-based malware. One of the major factors for enabling social malware is malicious third-party apps. We show that such malicious apps are also widespread in Facebook. Therefore, to identify malicious apps, we ask the question: given a Facebook application, can we determine if it is malicious? Our key contribution in this part is in developing malware detection in Facebook third party application by using Naïve Bayes algorithm technique .We identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Then, leveraging these distinguishing features, we show that can detect malicious apps with 99.5% accuracy, with no false positives and a low false negative rate (4.1%). Finally, we explore the ecosystem of malicious Facebook apps. We identify mechanisms these apps use to propagate and find that many apps collude and support each other.
format	Thesis
qualification_level	Master's degree
author	Angamuthu, Laavanya
author_facet	Angamuthu, Laavanya
author_sort	Angamuthu, Laavanya
title	Improving malicious detection rate for Facebook application in OSN platform
title_short	Improving malicious detection rate for Facebook application in OSN platform
title_full	Improving malicious detection rate for Facebook application in OSN platform
title_fullStr	Improving malicious detection rate for Facebook application in OSN platform
title_full_unstemmed	Improving malicious detection rate for Facebook application in OSN platform
title_sort	improving malicious detection rate for facebook application in osn platform
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/68968/1/FSKTM%202018%2041%20IR.pdf
_version_	1747812654504738816

Improving malicious detection rate for Facebook application in OSN platform

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