Phishing is a form of social engineering in which an attacker, also
known as a phisher, attempts to fraudulently retrieve legitimate users’
confidential or sensitive credentials by mimicking electronic
communications from a trustworthy or public organization in an automated
fashion. The word “phishing” appeared around 1995, when Internet
scammers were using email lures to “fish” for passwords and financial
information from the sea of Internet users; “ph” is a common hacker
replacement of “f”, which comes from the original form of hacking,
“phreaking” on telephone switches during 1960s. Early phishers copied
the code from the AOL website and crafted pages that looked like they
were a part of AOL, and sent spoofed emails or instant messages with a
link to this fake web page, asking potential victims to reveal their
passwords. The method based on available features on URL and page
contents without using the search engines such Google ets, to detect
the phishing websites where our methodology target to extract the most
number of features exist in literature then find the robust features
that are not affected by concept drift this is to answer the question
are there features can give the required accuracy when the training and
testing data come from different times? as the phishers changes their
tactics from time to time.
After
we find such features using Ant Colony Optimization, to examine the
performance and by applying classifier using Artificial Neural
Network(ANN),Support Vector Machine(SVM) and Treefit Algorithm to decide
which one give us the best performance .
The
performance analysis have to be done using software simulation
such as the Accuracy , Sensitivity and Selectivity and all
parameters related to examine the performance using Matlab.
Date set collection and Pre-processing
Data
sets should be implemented as shown in Figure, which shows the whole
data set collection and pre-processing process, the phishing websites
collected from PhishTank website in CSV format.
After generating the data sets required features given below,
Features:
1. having_IP_Address { 1,0 }
2. URL_Length { 1,0,-1 }
3. Shortining_Service { 0,1 }
4. having_At_Symbol { 0,1 }
5. double_slash_redirecting { 1,0 }
6. Prefix_Suffix { -1,0,1 }
7. having_Sub_Domain {
8. SSLfinal_State { -1,1,0 }
9. Domain_registeration_length { 0,1,
10. Favicon { 0,1 }
11. port { 0,1 }
12. HTTPS_token { 1,0 }
13. Request_URL { 1,-1 }
14. URL_of_Anchor { -1,0,1 }
15. Links_in_tags { 1,-1,0 }
16. SFH { -1,1 }
17. Submitting_to_email { 1,0 }
18. Abnormal_URL { 1,0 }
19. Redirect { 0,1 }
20. on_mouseover { 0,1 }
21. RightClick { 0,1 }
22. popUpWidnow { 0,1 }
23. Iframe { 0,1 }
24. age_of_domain { -1,0,1 }
25. DNSRecord { 1,0 }
26. web_traffic { -1,0,1 }
27. Page_Rank { -1,0,1 }
28. Google_Index { 0,1 }
29. Links_pointing_to_page { 1,0,-1 }
30. Statistical_report { 1,0 }
2. URL_Length { 1,0,-1 }
3. Shortining_Service { 0,1 }
4. having_At_Symbol { 0,1 }
5. double_slash_redirecting { 1,0 }
6. Prefix_Suffix { -1,0,1 }
7. having_Sub_Domain {
8. SSLfinal_State { -1,1,0 }
9. Domain_registeration_length { 0,1,
10. Favicon { 0,1 }
11. port { 0,1 }
12. HTTPS_token { 1,0 }
13. Request_URL { 1,-1 }
14. URL_of_Anchor { -1,0,1 }
15. Links_in_tags { 1,-1,0 }
16. SFH { -1,1 }
17. Submitting_to_email { 1,0 }
18. Abnormal_URL { 1,0 }
19. Redirect { 0,1 }
20. on_mouseover { 0,1 }
21. RightClick { 0,1 }
22. popUpWidnow { 0,1 }
23. Iframe { 0,1 }
24. age_of_domain { -1,0,1 }
25. DNSRecord { 1,0 }
26. web_traffic { -1,0,1 }
27. Page_Rank { -1,0,1 }
28. Google_Index { 0,1 }
29. Links_pointing_to_page { 1,0,-1 }
30. Statistical_report { 1,0 }
You can DOWNLOAD data-set details and reference papers.Contact us +91 7904568456 by whatsapp or sales@verilogcourseteam.com.
SIMULATION VIDEO DEMO
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