Скачать или смотреть DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING APPROACH

DESIGN DETAILS
While cybersecurity attacks continue to escalate in both scale and sophistication, social engineering approaches are still some of the simplest and most effective ways to gain access to sensitive or confidential information. The United States Computer Emergency Readiness Team (US-CERT) defines phishing as a form of social engineering that uses e-mails or malicious websites to solicit personal information from an individual or company by posing as a trustworthy organization or entity. While organizations should educate employees about how to recognize phishing e-mails or links to help protect against the above types of attacks, software such as HTTrack is readily available for users to duplicate entire websites for their own purposes. As a result, even trained users can still be tricked into revealing private or sensitive information by interacting with a malicious website that they believe to be legitimate. This Matlab design is to develop methods of utilizing various approaches to categorize websites specifically uses machine learning techniques to classify websites based on their URL. Four classifiers used in the design,
1.Decision Tree,
2.Naive Bayesian classifier,
3.Support Vector Machine (SVM), and
4.Neural Network.

The classifiers were tested with a data set containing 30 features from real world URLs where each could be categorized as below,
feature_terms{1}='having_IP_Address';
feature_terms{2}='URL_Length';
feature_terms{3}='Shortining_Service';
feature_terms{4}='having_At_Symbol';
feature_terms{5}='double_slash_redirecting';
feature_terms{6}='Prefix_Suffix';
feature_terms{7}='having_Sub_Domain';
feature_terms{8}='SSLfinal_State';
feature_terms{9}='Domain_registeration_length';
feature_terms{10}='Favicon';
feature_terms{11}='port';
feature_terms{12}='HTTPS token';
feature_terms{13}='Request_URL';
feature_terms{14}='URL_of_Anchor';
feature_terms{15}='Links_in_tags';
feature_terms{16}='SFH';
feature_terms{17}='Submitting_to_email';
feature_terms{18}='Abnormal_URL';
feature_terms{19}='Redirect';
feature_terms{20}='on_mouseover';
feature_terms{21}='RightClick';
feature_terms{22}='popUpWidnow';
feature_terms{23}='Iframe';
feature_terms{24}='age_of_domain';
feature_terms{25}='DNSRecord';
feature_terms{26}='web_traffic';
feature_terms{27}='Page_Rank';
feature_terms{28}='Google_Index';
feature_terms{29}='Links_pointing_to_page';
feature_terms{30}='Statistical_report';

REFERENCES
Reference Paper-1: Phishing Websites Detection using Machine Learning.
Author’s Name: Arun Kulkarni and, Leonard L. Brown
Source: IJACSA
Year: 2019

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