Identification of Malicious Facebook's Application

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 10 | March 2017 ISSN (online): 2349-6010

Identification of Malicious Facebook’s Applications Eshan Bhatt Department of Information Technology K.J. Somaiya Institute of Engineering & Information Technology, Sion, Mumbai, Maharashtra, India.

Smit Kotadia Department of Information Technology K.J. Somaiya Institute of Engineering & Information Technology, Sion, Mumbai, Maharashtra, India.

Abstract Our given paper is based on web security for Facebook users that access and use Facebook applications and are unaware of the security threats that these applications pose. In the given paper we identify such malicious applications not only based on their general characteristics, but also the specific parameters of the applications, identified based on the study. Malicious applications [1] can be identified and the user that wishes to access the particular application is alerted even before the application is installed on to the user profile. This prevents the user’s security from being harmed by the application. Also the proposed system identifies the malicious applications that are not very popular unlike the existing system. The malicious applications harm the security and the privacy of the vast user base of the social networking giant- Facebook, and this is points that have been taken into consideration in the given paper.

Keywords: Malicious, Applications, malignant, spam, online social network _______________________________________________________________________________________________________ I.

INTRODUCTION

Social networking websites have now become a very important aspect of the modern times. The advantages of social networking websites range from easy access to data to a connection with a person who is thousands of miles away. Nevertheless, where there are advantages, lies about risks with it. After looking at the inexorable growth of social networking giants – like Facebook, the unscrupulous hackers have now directed their attention towards the massive user base of almost 500 million users of Facebook and have started attacking user’s private data and for the same, hackers use the element of Facebook’s Applications as the medium to attack the users [3]. The amount and detail of private data stored in user profiles on these networks makes an attractive target for marketing companies, spammers, spear phishers, and identity thieves. In the given paper, we strain to find out and answer the question that is it possible to discover the malicious Facebook’s applications from the non-malicious applications? Facebook applications are a vital part of Facebook. Different categories of applications exist, from games to utility based. However, besides the usefulness of these applications, lie certain unseen risks like the user’s private data fetching and posting some content without the consent of the user. Based on the study, we know that the characteristics of the malicious and non-malicious applications differ significantly, and in the proposed system, we identify these characteristics, using which we identify [2] the different applications as malicious and non-malicious.

II. SURVEY Based on the report and investigation of existing system, the previous system used to identify the malicious applications on the Facebook only after they have performed their intended actions on the user's wall. However, the initial users still get affected.

However, we have added an additional feature than the previous one. In the proposed system, we are able to identify the malicious application even before the users allow the application and give permission to the application for using it. Once the application got the permission set required by the hacker, they post content on the user wall. Neither the existing system have any sort of service through which the user could know before using the application, that whether it is benign or not. Even if the user wants to check whether the application actually existed in Facebook or not, the user had to submit the application id to Facebook and thus check it. A primary study to calculate and analyze spam campaigns launched on online social networks. They calculated a huge anonymized dataset of asynchronous “ wall” messages in between Facebook users. System detected generally 200,000 malicious wall posts with embedded URLs, originating from more than 57,000 user accounts. The study revealed that the 97% of the malicious accounts were compromised accounts [8]. Also a way to identify that whether the application is malicious or benign, the user takes the help of the community ratings, which are not reliable for identifying privacy risks a application creates [9]. Also, the study reveals that 60% of the malicious applications get at least one hundred thousand clicks [10]. In this system, we are alerting the user to even before the application is installed on to the particular user profile. Thus, preventing the user from the hacker’s intent to hurt the user’s privacy or any other malicious intent. Also, the application is not just classified based the posts that application puts up on behalf of the user, but it also identifies the parameters of the application based on which the application can be classified as safe or not safe. These parameters are also decided based on the study that has been attempted on a raft of applications. Since the given system identifies the application before it gets installed into the

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Identification of Malicious Facebook’s Applications (IJIRST/ Volume 3 / Issue 10/ 023)

profile of user, there is no chance that the user’s privacy data can be harmed by the application or the hacker. Thus, giving premium security to the user’s profile on Facebook.

III. TECHNOLOGY USED IN EXISTING SYSTEM There are many ways that hackers can benefit from a malicious app: 1) The app can reach large numbers of users and their friends to spread spam [7], 2) The app can obtain users’ personal information such as email address, home town, and gender, and 3) The app can “ re-produce" by making other malicious apps popular. To make matters worse, the deployment of malicious apps is simplified by ready-to-use toolkits. In other words, there is motive and opportunity, and as a result, there are many malicious apps spreading on web applications every day.

In the present system, there is an application called as My Page Keeper; which monitors the profiles of the Facebook users and identifies the malicious url posted by the malicious Facebook’s applications on the users wall. Then it marks that application and badges it as malicious.

There was a study conducted, that consisted a constant monitoring of 91 million posts from 111k Facebook’s applications. In this study, if any application’s post was found and identified malicious by My Page Keeper [4], then it marked that application as malicious. Based on this heuristic, 6350 applications were found malicious. However, it is found that, malicious applications that were not very famous and did not appear on myriad Facebook’s user’s wall were not classified as malicious. However, the proposed system identifies this problem and identifies malicious applications even if they are not popular.

IV. SYSTEM ARCHITECTURE OF PROPOSED SYSTEM Proposed system is divided into two main modules. The first module is identifying application and second one is report the malicious application to user. The architecture of proposed system is shown in following figure.

Identification of application: Following are the sequence of steps for identification of apps. The system is initialized by the user. User needs to sign up with basic details such as email id and password where email id will be a unique primary key. These details are stored in facbook server. Many users are using facebook so anyone one can upload application in facebook.to upload apps in facebook permission are needed .accepting these request apps are upload on facebook. For these we used SVM (support vector machine) classifier method to identify application. SVM classifier detects the application and performing some classifying method. Based on that it will decide the apps are malicious or not. If malicious apps are found then it will block the application. These malicious apps are identify by certain parameter [6] .if benign apps are found then it will be accessed by the user.

App Upload Request

App Upload Facebook

On

Classi fy

Malicious

Benign

Block

Use Application

Stop Fig. 1: Flow Diagram of proposed system for identification of application

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Identification of Malicious Facebook’s Applications (IJIRST/ Volume 3 / Issue 10/ 023)

Report Application: In this client communicate to application server for adding application on the facebook. Application server is act as middleware between user and facebook server. Application server check the application based on certain parameter and identify that this app is malicious or benign. If it respond 1M (malicious app) then application server shows alert message on screen [5].

Request

Application server

Client

Check

Alert

Malicious benign

1M

Fig. 2: Flow Diagram of report application to user Existing System It allow user to access malicious app on facebook[1] In existing there is no security of user data Focuses on identifying malicious applications based on posts and reviews[] Cannot prevent attack from unpopular malicious application Hacker try to advertise application through third party apps The app can obtain users’ personal information such as email address, home town, and gender.

Proposed System Prevents accessing malicious application It provides a better security to user data as compared to the existing system Focuses on quantifying, profiling, and understanding and then classifying malicious apps Prevent the malicious attack from hacker.

Restriction of advertisement from third party apps and spamming of application System can detect malicious apps with higher accuracy and prevent the access of private data by the application.

V. COMPARISON BETWEEN EXISTING SYSTEM AND PROPOSED SYSTEM. The detailed study and the analyses of the applications helped us to reach a conclusion in which we were able to identify certain apocryphal characteristics of the applications which and thus we identified the parameters of the applications for classification.

Also, we identified that the existing system was only able to identify that malicious applications that were already popular among the users of Facebook and failing to identify the applications that were not so popular. The proposed system identifies this flaw and acts accordingly.

VI. CONCLUSION In this paper is written with help of the base paper “ Detecting Malicious Facebook Applications. Applications present convenient means for hackers to spread malicious content on Facebook. However, little is understood about the characteristics of malicious apps and how they operate. In this paper, an analysis of a large entity of malicious Facebook apps is observed and it is found that malicious apps differ significantly from benevolent apps with respect to some features. For example, malicious apps are much more likely to share names with other apps, and they typically ask for fewer Permissions than benevolent apps.

Leveraging our observations, System is developed, an accurate SVM classifier for detecting malicious Facebook applications.

We hope that Facebook will benefit from our recommendations for reducing the threaten of hackers on their platform.

ACKNOWLEDGEMENT We wish to express our sincere gratitude to Mr. Harsh Bhor, Project Guide for providing us an opportunity to do our project work in Web Security domain. We sincerely thank Mr. Uday Rote, HOD of IT Department and Mr. Harsh Bhor, Project Coordinator for their guidance and encouragement in carrying out this project work. We also wish to express our gratitude to the officials and other staff members of K.J Somaiya Institute of Engineering and Information Technology, who rendered their help during the period of our project work.

REFERENCE [1] [2] [3] [4] [5]

Sazzadur Rahman,Ting-kai Humang,Michalis Faloutsos Detecting malicious facebook application IEEE/ACM Transaction on networking ,IEEE conference , year 2016,volume-24.

K. Thomas, C. Grier, J. Ma, V. Paxson and D. Song Design and evaluation of a real-time URL spam filtering service, IEEE/ACM symp, in year 2015.

Rahman M S, T.-K. Huang, H. V. Madhyastha, and M. Faloutsos, “Efficient and scalable software detection in online social networks,” in Proc. USENIX Security, year 2012 ApP piggybacking example.

31TUhttps://apps.facebook.com/mypageke eper/U31T?status=scam_report_fb_survey_scam_ Converse_shoes_2012_05_17_boQ. Bitdefender Safego. http: //www.facebook.com/bitdefender.safego.

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Identification of Malicious Facebook’s Applications (IJIRST/ Volume 3 / Issue 10/ 023) [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

H. Gao et al., “ Detecting and characterizing social spam campaigns, in Proc. IMC, 2010, pp. 3547.

H. Gao, Y. Chen, K. Lee, D. Palsetia, and A. Choudhary, Towards online spam filtering in social networks, in Proc. NDSS, 2012.

Hongyu Gao, Jun Hu, Christo Wilson,Zhichun Li, Yan Chen, Ben Y. Zhao Detecting and Characterizing Social Spam Campaigns Pern Hui Chia, Yusuke Yamamoto, N.Asokan Is this App Safe? A Large Scale Study on Application Permissions and Risk Signals .

Sushma Nallamalli, Loya Chandrajit Yadav, Siva Parvathi, Karicharla Prasad A Survey on Detecting Malicious Facebook Applications using FRAppE G. Cluley The Pink Facebook rogue application and survey scam, 2012 [Online].

G. Stringhini, C. Kruegel, and G. Vigna. Detecting spammers on social networks.

H. Gao, Y. Chen, K. Lee, D. Palsetia, and A. Choudhary. Towards online spam filtering in social networks 2012.

J. King, A. Lampinen, and A. Smolen. “Privacy: Is there an app for that? In SOUPS, 2011.

Y. Liu, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Analyzing facebook privacy settings: user expectations vs. reality. In IMC, 2011

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