mlwithphishing's Introduction

ML with Phishing

The files in this repo will be used for my "Machine Learning with Phishing" series on my blog, please refer to the original blog posts to have a better understanding of this project.

The dataset used contains the 10 'baseline features' that were selected in this study.

Features

Here are the features used for this project:

No.	Identifier	Value type	Description
1	NumDash	Discrete	Counts the number of "-" in webpage URL.
2	NumNumericChars	Discrete	Counts the number of numeric characters in the webpage URL.
3	NumSensitiveWords	Discrete	Counts the number of sensitive words (i.e., "secure", "account", "webscr", "login","ebayisapi", "signin", "banking", "confirm") in webpage URL.
4	PctExtHyperlinks	Continuous	Counts the percentage of external hyperlinks in webpage HTML source code.
5	PctNullSelfRedirectHyperlinks	Continuous	Counts the percentage of hyperlinks fields containing empty value, self-redirect value such as "#", the URL of current webpage, or some abnormal value such as "file://E:/".
6	FrequentDomainNameMismatch	Binary	Checks if the most frequent domain name in HTML source code does not match the webpage URL domain name.
7	SubmitInfoToEmail	Binary	Check if HTML source code contains the HTML "mailto" function.
8	PctExtResourceUrlsRT	Categorical	Counts the percentage of external resource URLs in webpage HTML source code. Apply rules and thresholds to generate value.
9	ExtMetaScriptLinkRT	Categorical	Counts percentage of meta, script and link tags containing external URL in the attributes. Apply rules and thresholds to generate value.
10	PctExtNullSelfRedirectHyperlinksRT	Categorical	Counts the percentage of hyperlinks in HTML source code that uses different domain names, starts with "#", or using "JavaScript ::void(0)". Apply rules and thresholds to generate value.

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