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Phishing detection & protection scheme

Presentation provides an antiphishing approach

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Phishing detection & protection scheme

  1. 1. Presented By: Shaikh Mussavir Ahemad SGGS IE &T, Nanded Intelligent Phishing detection & protection scheme for online Transaction
  2. 2. Outline  Introduction  Methodology  Feature extraction & analysis  Experimental procedures  Conclusions & future work  References  Questions
  3. 3. Introduction  What is phishing ?  Phishing basics  Phishing information flow  Visually similar Webpages  Growth rate of phishing sites  Approaches of anti phishing  Objectives of Study
  4. 4. What is Phishing? Definition  Phishing is an act to fraudulently acquire user’s sensitive information such as password, credit/debit card number through illegal website that look exactly like target website
  5. 5. Phishing basics  Visually similar website  Email containing time constraint  Fake https certificate  Attractive offers one phishing webpage  Attractive games containing link to the phishing webpage
  6. 6. Figure:Phishing information flow
  7. 7. Visually similar websites
  8. 8. Growth rate of phishing sites According to UK cards association press release report:  Phishing attacks caused $21.6 million loss between January & June 2012  A growth of 28% from June 2011  Number of websites detected by APWG 63,253 /month
  9. 9. Growth rate of phishing sites  Number of URLs 1,75,229  Significant growth caused by huge number of phishing websites created by criminals for financial benefits  Phishing techniques are improved regularly & getting more sophisticated
  10. 10. Approaches of Antiphishing Antiphishing approaches are developed to combat the problem of phishing The existing approaches are Feature based Content based URL blacklist based
  11. 11. Objectives of approach  Identify & extract phishing features based on five inputs  Develop a neuro fuzzy model  Train & validate the fuzzy inference model on real time  Maximizing the accuracy of performance and minimizing false positive & operation time
  12. 12. Methodology Proposed approach utilize Neuro Fuzzy with five inputs  Neuro fuzzy  Five inputs
  13. 13. Neuro Fuzzy  Combination of fuzzy logic & neural network Neuro fuzzy = Fuzzy logic + Neural network  Allows use of numeric & linguistic properties  Allows Universal approximation with ability to use fuzzy IF......Then rules  Fuzzy logic deal with reasoning on higher level using numerical and linguistic information from domain expert  Neural network perform well when dealing with raw data
  14. 14. Five Inputs  Five inputs are five tables where features are extracted and stored for references  Wholly representative of phishing attack technique and strategies  288 features are extracted from these inputs i. Legitimate site rules ii. User behavioral profile iii. Phish tank iv. User specific sites v. Pop up from email
  15. 15. Five Inputs  Legitimate site rules Summary of law covering phishing crime  User behavioral profile List of people behavior when interacting with phishing websites  Phish tank Free community website where suspected websites are verified and voted as a phish by community experts
  16. 16. Five Inputs  User specific sites Contains binding information between user and online transaction service provider  Pop-Ups from Email Pop-Ups from email are general phrases used by phishers
  17. 17. Feature Extraction And Analysis  Extraction is based on the five inputs  An automated wizard is used to extract features and store in excel sheet as phishing techniques evolve with time  Legitimate site rules consist of 66 extracted features  Based on user behavior profile 60 features are extracted  Likewise phish tank carries 72 features that are extracted by exploring 200 phishing websites from phish tank archive
  18. 18. Feature Extraction And Analysis  Also user specific sites have 48 features extracted by consulting with bank experts & 20 legal websites  Equally pop-ups from email consist of 42 features gathered by observing pop-ups on screen  These total 288 feature also known as data  This data is used to differentiate between phishing ,legitimate and suspicious websites accurately  Most frequent terms are searched by using ‘FIND’ function
  19. 19. Feature Extraction And Analysis  Consequently the terms that appear often are assigned a value from 0 to 1 that is phishing website= 1 Legitimate website= 0 Suspicious website = Any number between 0 to 1  This strategy facilitate accuracy & reduces complexity in fuzzy rules
  20. 20. Figure: Intelligent phishing detection system overall process diagram
  21. 21. Experimental Procedure Training and testing methods  2 fold cross validation method is used to train and test the accuracy and robustness of the proposed model  Divides data into two parts i. Training is done on part I ii. Testing is done on part II  Then the role of training and testing is reversed  Finally the results are assembled
  22. 22. Conclusion And Future Work  Study presented is based on neural fuzzy scheme to detect phishing websites & protect customers performing online transactions on those sites  Using 2 fold cross validation the proposed scheme with five input offer a high accuracy in detecting phishing sites in real time  Scheme offers better performance in comparison to previously reported research  Primary contribution of this research is the framework of five input which are the most important elements of this research
  23. 23. Continue….  Future work is adding more feature & parameters optimization for a 100% accuracy to develop a plug in toolbar for real time application
  24. 24. References 1. Intelligent phishing detection and protection scheme for online transacti Original Research Article Expert Systems with Applications, Volume 40, Issue 11, 1 September 2013, Pages 4697-4706 P.A. Barraclough, M.A. Hossain, M.A. Tahir, G. Sexton, N. Aslam 2. Intelligent phishing detection system for e-banking using fuzzy data mini Original Research Article Expert Systems with Applications, Volume 37, Issue 12, December 2010, Pages 7913-7921 Maher Aburrous, M.A. Hossain, Keshav Dahal, Fadi Thabtah
  25. 25. Any Questions??Any Questions??
  26. 26. ThankThank You...You...