International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Detecting Malicious Facebook Applications Sreeja Krishna V R1, Lavannya Varghese2 1Student
Dept of computer science St.Joseph’s college irinjalakkuda Dept of computer science St.Joseph’s college irinjalakkuda ---------------------------------------------------------------------***--------------------------------------------------------------------2Professer
Abstract - With daily installs, third-party Apps can be a
important cause for the popularity and attractiveness of Facebook or any online social media. Sadly, cyber criminals get came to the realization that the capability of using apps for spreading spam and malware. We realize that at the least 13% of Facebook apps in the dataset are usually malevolent. However with their findings , several issues like faux profiles, malicious application have conjointly full-grown. There aren't any possible method exist to regulate these issues. During this project, we tend to came up with a framework with that automatic detection of malicious applications is feasible and is efficient. Suppose there's Facebook application, will the Facebook user verify that the app is malicious or not. In fact the Facebook user cannot establish that therefore The key contribution is in developing FRAppE-Facebook’s Rigorous Application Evaluator is the first tool focused on detecting malicious apps on Facebook. To develop FRAppE, we tend to use data gathered by the posting behavior of Facebook apps seen across million users on Facebook. First we identify a set of features that help us to analyze malicious from benign ones. Second, leveraging these distinguishing features ,where we show that FRAppE can detect malicious apps with 95.9% accuracy. Finally, we explore the ecosystems of malicious Facebook apps and identify mechanisms that these apps use to spread. Key Words: apps, malicious, Online social networks.
1.INTRODUCTION The new battleground for cybercrime is Online Social Networks (OSNs), which provides a new, fertile, and unexplored environment for the dissemination of malware. A social networking website may be a web site wherever every user contains a profile and might keep contact with friends, share their updates, meet new people that have a same interests. . Moving beyond spam email, the spread of malware on OSNs takes the form of postings and communications between friends. We use the term social malware to describe damaging behaviour including identity theft, distribution of malicious URLs, spam, and malicious apps that utilizes OSNs. The use of posts from friends adds a powerful element in the propagation of social malware: it comes implicitly with the endorsement of a friend who reputedly posts the information. These Online social networks (OSN) enable third party apps to enhance the user experience on the platforms. Such enrichment includes interesting or entertaining ways of communicating among © 2017, IRJET
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Impact Factor value: 5.181
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online friends and different activities such as playing games , listening songs. Recently, hackers have started taking advantage of the recognition of this third-party apps platform and deploying malicious applications. There are many ways that hacker can benefit from a malicious apps. Some of the ways are: the app can reach large numbers of users and their friends to spread spam, the app can obtain users’ personal information such as email address, home town, and gender, and the app can “reproduce" by making other malicious apps popular. Therefore, it is becoming increasingly important to understand social malware better and build better defences to protect users from the crime underlying this social malware. Detecting social malware needs novel approaches since hackers use extremely different approaches in its distribution compared to email-based spam. For example, reputation-based filtering is insufficient to finf social malware received from friends and the keywords used in email spam significantly differ from those used in social malware. We also find that URL blacklists designed to detect phishing and malware on the web do not suffice, e.g., because a large fraction of social malware (26% in our dataset) points to malicious applications hosted on Facebook. Although such malicious apps are widespread in Facebook, as we show later, currently there is no commercial service, publicly-available information, or research-based tool to advise a user about the risks of an app. In this paper we develop FRAppE, a suite of efficient classification techniques for identifying whether an app is malicious or not. This is arguably the first comprehensive study focusing on malicious Facebook apps that focuses on quantifying, profiling, and understanding malicious apps, and synthesizes this information into an effective detection approach. The basis of our study is a dataset. We classify url as social spam if it points to a web page that spread malware, attempts to phish, request to carry a task, false promises etc. We systematically profile apps and show that malicious app profiles are significantly different than those of benign apps. A striking observation is the laziness" of hackers; many malicious apps have the same name, as 8% of unique names of malicious apps are each used by more than 10 different apps (as defined by their app IDs). Overall, we profile apps based on two classes of features: (a) those that can be obtained on-demand given an application's identifier (e.g., the permissions required by the app and the posts in the application's profile page), and (b) others that require a ISO 9001:2008 Certified Journal
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