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an Open Access Publication ISSN: 0976-9730 | 0976-9498

Engineering and Technology

Attack on Camera and Finding Fraud Applications in App Store

Miss.Chaskar Prachi D., Miss.Dangat Pooja A.,Miss.Guldhekar Divy a S., Miss.Sukale Shubhangi R,G Swati.S.
1Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India prachichaskar9696@gmail.com 2Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India pooja14dangat@gmail.com 3Dept. of Computer Engineering Jaihind Collage oF Engineering, Kuran Pune, India divyaguldhekar10@gmail.com 4Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India shubhangisukale21@gmail.com 5Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India swatigore9@gmail.com
Miss.Chaskar Prachi D., Miss.Dangat Pooja A.,Miss.Guldhekar Divy a S., Miss.Sukale Shubhangi R,G Swati.S.
1Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India prachichaskar9696@gmail.com 2Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India pooja14dangat@gmail.com 3Dept. of Computer Engineering Jaihind Collage oF Engineering, Kuran Pune, India divyaguldhekar10@gmail.com 4Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India shubhangisukale21@gmail.com 5Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India swatigore9@gmail.com
Online First: February 21, 2018
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Abstract

System focus on the security issues related to camera based attacks on smart phones. The fraudulent application and its traitor can be detected by using the defense system that detects the attacks. To evaluate the defense system we have proposed a camera based fraudulent application for feasibility study and also we analyzed the performance of the system with the fraudulent applications from the open android market. To download application smart phone user has to visit play store. When user visit play store then he is able to see the various application lists. This list is built on the basis of promotion or advertisement. User does not have knowledge about the application. So user looks at the list and downloads the applications. But sometimes it happens that the downloaded application won't work or not useful. That means it is fraud in mobile application list. We are going to find the applications those are fraud, from Google play store. We are providing sentiment analysis on the reviews, for finding true positive and false negative of the reviews and also working on NLP algorithm from which we are finding positive comments, negative comments and neutral comments.   

Keyword : attacks, NLP , Fraudulent Application, Defense System, Android, Security and Privacy, Google Play Store, Camera Based Attack, Mobile Application, App Rating.

  Submitted
Feb 21, 2018
Published
Feb 21, 2018
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References

1. Jonathan Crussell, Ryan Stevens, Hao Chen, ”MAdFraud: Investigating Ad Fraud in Android Applications” June 16/9, 2014, Bretton Woods, New Hampshire, USA. 2. Raghuveer Dagade, Prof. Lomesh Ahire, ”Review: A Ranking Fraud Detection System for Mobile Apps” International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 11, November 2015. 3. Bin Liu, Ramesh Govindan, ”DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps” April 2, 2014 Seattle, WA, USA ISBN 978-1-931971-09-6. 4. Longfei Wu and Xiaojiang Du, Temple University Xinwen Fu, University of Massachusetts Lowell, ”Security Threats to Mobile Multimedia Applications: Camera- Based Attacks on Mobile Phones”,IEEE Communications Magazine March 2014. 5. Y. Zhou and X. Jiang, ”Dissecting Android Malware: Characterization and Evolution”, IEEE Symp. Security and Privacy 2012, 2012, pp. 978-1-5090-1877-2/16 IEEE. 6. F. Maggi, et al., ”A Fast Eavesdropping Attack against Touchscreens”,7th Int. Conf.Info. Assurance and Security, 2011,Print ISBN: 978-1-4577-2154-0 . 7. Shagufta Md.Rafique Bagwan, Prof. L.J.Sankpal, ”VisualPal: A Mobile App for Object Recognition for the Visually Impaired” 2015, IEEE International Conference on Computer, Communication and Control (IC4-2015). 8. Chia-Mei Chen, Je-Ming Lin, Gu-Hsin Lai,National Sun Yat-sen University Kaohsiung, Taiwan ”Detecting Mobile Application Malicious Behaviors Based on Data Flow of Source Code”,2014 International Conference on Trustworthy Systems and their Applications. 9. Su Mon Kywe, Yingjiu Li, Jason Hong, Cheng Yao,Carnegie Mellon University, ”Dissecting Developer Policy Violating Apps: Characterization and Detection”,2016 11th International Conference on Malicious and Unwanted Software: Know Your Enemy?(MALWARE),(2016). 10. Nai-Wei Lo, Kuo-Hui Yeh, and Chuan-Yen Fan ”Leakage Detection and Risk Assessment on Privacy for Android Applications: LRPdroid” 1932-8184 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission,(2014). 11. Tae Oh, Suyash Jadhav, Young Ho Kim, ”Android botnet categorization and family detection based on behavioral and signature data” Cyber Security System Research Dept. Electronics and Telecommunication Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700,KOREA ,(2015).
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References

1. Jonathan Crussell, Ryan Stevens, Hao Chen, ”MAdFraud: Investigating Ad Fraud in Android Applications” June 16/9, 2014, Bretton Woods, New Hampshire, USA.
2. Raghuveer Dagade, Prof. Lomesh Ahire, ”Review: A Ranking Fraud Detection System for Mobile Apps” International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 11, November 2015.
3. Bin Liu, Ramesh Govindan, ”DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps” April 2, 2014 Seattle, WA, USA ISBN 978-1-931971-09-6.
4. Longfei Wu and Xiaojiang Du, Temple University Xinwen Fu, University of Massachusetts Lowell, ”Security Threats to Mobile Multimedia Applications: Camera- Based Attacks on Mobile Phones”,IEEE Communications Magazine March 2014.
5. Y. Zhou and X. Jiang, ”Dissecting Android Malware: Characterization and Evolution”, IEEE Symp. Security and Privacy 2012, 2012, pp. 978-1-5090-1877-2/16 IEEE.
6. F. Maggi, et al., ”A Fast Eavesdropping Attack against Touchscreens”,7th Int. Conf.Info. Assurance and Security, 2011,Print ISBN: 978-1-4577-2154-0 .
7. Shagufta Md.Rafique Bagwan, Prof. L.J.Sankpal, ”VisualPal: A Mobile App for Object Recognition for the Visually Impaired” 2015, IEEE International Conference on Computer, Communication and Control (IC4-2015).
8. Chia-Mei Chen, Je-Ming Lin, Gu-Hsin Lai,National Sun Yat-sen University Kaohsiung, Taiwan ”Detecting Mobile Application Malicious Behaviors Based on Data Flow of Source Code”,2014 International Conference on Trustworthy Systems and their Applications.
9. Su Mon Kywe, Yingjiu Li, Jason Hong, Cheng Yao,Carnegie Mellon University, ”Dissecting Developer Policy Violating Apps: Characterization and Detection”,2016 11th International Conference on Malicious and Unwanted Software: Know Your Enemy?(MALWARE),(2016).
10. Nai-Wei Lo, Kuo-Hui Yeh, and Chuan-Yen Fan ”Leakage Detection and Risk Assessment on Privacy for Android Applications: LRPdroid” 1932-8184 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission,(2014).
11. Tae Oh, Suyash Jadhav, Young Ho Kim, ”Android botnet categorization and family detection based on behavioral and signature data” Cyber Security System Research Dept. Electronics and Telecommunication Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700,KOREA ,(2015).
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