Upload
abhishek-gupta
View
162
Download
1
Embed Size (px)
Citation preview
SRI REVANA SIDDESHWARA INSTITUTE OF TECHNOLOGY
Chokkanahalli, Chikkajala, International Airport RoadBangalore – 562157
Department Of Computer Science & Engineering
PROJECT PHASE -3 PRESENTATION
1
FRAPPE: SCANNING FACEBOOK APPLICATIONS FOR SPYWARES AND BACK DOORS Guided By:
Prof. Arpitha T. R
Prepared By:Abhishek Gupta (1RC12CS001)Meghashree S. P (1RC12CS018)Pavithra S (1RC12CS023)Shruthi S (1RC12CS036)
THE DIARY Introduction
Abstract
Existing System
Proposed System
System Architecture
Requirements
Module
Use Case Diagram
Activity Diagram
Sequence Diagram
Snapshots
Conclusion
References
SRSIT CSE
INTRODUCTION
Online Social Networks(OSNs) enable and encourage third party applications to enhance the user experience on these platforms.
Facebook provides developers an API that facilitates app integration into the user-experience.
There are 500k apps available on Facebook and it has average of 20 million installs per day.
Due to the popularity of the apps in Facebook, Hackers are taking advantage to spread spam.
FRAppE is a tool used to detect malicious apps with 99% accuracy.
SRSIT CSE
ABSTRACT
Developing an tool called FRAppE, an accurate classifier for detecting malicious Facebook applications.
Most interestingly, we highlighted the emergence of app-nets large groups of tightly connected applications that promote each other.
Continue to dig deeper into this ecosystem of malicious apps on Facebook, and we hope that Facebook will benefit from our recommendations for reducing the menace of hackers on their platforms.
SRSIT CSE
EXISTING SYSTEM Most research's are related on detecting spam posts and malware. Identifying spam campaigns. A honey-pot-based approach to detect spam accounts on OSNs. Personal information or surveys can be sold to third parties to eventually
profit the hackers.
SRSIT CSE
DISADVANTAGES OF EXISTING SYSTEM
Malicious apps are out of control on Facebook and indicates that they do not operate in isolation.
Find that malicious apps collude at scale many malicious apps share the same name, several of them redirect to the same domain upon installation.
Observed behaviors indicate well-organized crime, with a few prolific hacker groups controlling many malicious apps.
SRSIT CSE
PROPOSED SYSTEM Develop efficient techniques for online spam filtering on OSNs such as
Facebook. A third-party application for spam detection on Facebook mechanisms for
detection of spam URLs on Facebook. Rather than classifying individual URLs or post as spam. We focus on
identifying malicious applications that are the main source of spam on Facebook.
Also similarly developed application converging the techniques to identify accounts of spammers on Facebook.
SRSIT CSE
ADVANTAGES OF PROPOSED SYSTEM
It focuses on quantifying, profiling & understanding malicious apps. User information's are secured and safe. Avoiding use of different client IDs in app installation. FRAppE can detect malicious apps with 99% accuracy.
SRSIT CSE
SYSTEM ARCHITECTURE
SRSIT CSE
SYSTEM MODEL
SRSIT CSE
HARDWARE REQUIREMENTS
System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb.
SRSIT CSE
SOFTWARE REQUIREMENTS
Operating system : Windows XP/7. Coding Language : JAVA/J2EE IDE : Net beans 7.4 Database : MYSQL
SRSIT CSE
14
MODULES
Data collection Feature extraction Training Classification & Detection
SRSIT CSE
15
DATA COLLECTION
The data collection component has two subcomponents:
The collection of facebook apps with URLs Crawling for URL redirections
The crawling thread appends these retrieved URL and IP chains to the tweet information and pushes it into a queue.
SRSIT CSE
16
FEATURE EXTRACTION
The feature extraction component has three subcomponents: Grouping of identical domains Finding entry point URLs Extracting feature vectors
To classify a post, MyPageKeeper evaluates every embedded URL in the post
SRSIT CSE
17
TRAINING
The training component has two subcomponents: Retrieval of account statuses Training of the classifier
To label the training vectors, we use the account status; URLs from suspended accounts are considered malicious whereas URLs from active accounts are considered benign.
We periodically update our classifier using labeled training vectors.
SRSIT CSE
18
USE CASE DIAGRAMSRSIT CSE
19
ACTIVITY DIAGRAMSRSIT CSE
20
SEQUENCE DIAGRAMSRSIT CSE
21
SRSIT CSE
SNAPSHOTS
22
SRSIT CSE
23
SRSIT CSE
24
SRSIT CSE
25
SRSIT CSE
26
SRSIT CSE
27
CONCLUSION
Applications present convenient means for hackers to spread malicious content on Facebook.
little is understood about the characteristics of malicious apps and how they operate.
we showed that malicious apps differ significantly from benign apps with respect to several features
we developed FRAppE, an accurate classifier for detecting malicious Facebook applications
SRSIT CSE
REFERENCES [1] C. Pring, “100 social media statistics for 2012,” 2012 [Online]. Available:
http://thesocialskinny.com/100-social-media-statistics-for-2012/ [2] Facebook, Palo Alto, CA, USA, “Facebook Open graph API,” [Online]
Available:http://developers.facebook.com/docs/reference/api/ [3] “Wiki: Facebook platform,” 2014 [Online]. Available: http://en.
wikipedia.org/wiki/ Facebook Platform [4] “Profile stalker: Rogue Facebook application,” 2012 [Online]. Available:
https://apps.facebook.com/mypagekeeper/?status=scam_report- _fb_survey_scam_pr0file_viewer_2012_4_4 .
SRSIT CSE
29
THANK YOU..!!
Queries..??