Report : 鄭志欣Advisor: Hsing-Kuo Pao

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Learning to Detect Phishing Emails

I. Fette, N. Sadeh, and A. Tomasic. Learning to detect phishing emails. In Proceedings of the International World Wide Web Conference (WWW), pages 649–656, 2007.

Outline

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Introduction MethodEmpirical evaluationConclusion

Introduction

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Phishing (Spoofed websites)Stealing account informationLogon credentialsIdentity information

Phishing Problem – Hard

Method

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PILFER – A Machine Learning based approach to classification.phishing emails / ham (good) emailsFeature Set

Features as used in email classification

Features as used in email classification

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IP-based URLs:http://192.168.0.1/paypal.cgi?fix_account Phishing attacks are hosted off of

compromised PCs. This feature is binary.

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Age of linked-to domain namesLegitimate-sounding domain name

Palypal.com paypal-update.com

These domains often have a limited life WHOIS query

date is within 60 days of the date the email was sent – “fresh” domain.

This is a binary feature

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Nonmatching URLsThis is a case of a link that says paypal.com

but actually links to badsite.com.

Such a link looks like <a href="badsite.com"> paypal.com</a>.

This is a binary feature.

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“Here” links to non-modal domain“Click here to restore your account access”

Link with the text “link”, “click”, or “here” that links to a domain other than this “modal domain”

This is a binary feature.

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HTML emailsEmails are sent as either plain text, HTML, or

a combination of the two - multipart/alternative format.

To launch an attack without using HTML is difficult.

This is a binary feature.

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Number of linksThe number of links present in an email.

<a> in HTML tag

This is a continuous feature.

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Number of domainsSimply take the domain names previously

extracted from all of the links, and simply count the number of distinct domains.

Look at the “main” part of a domain https://www.cs.university.edu/ http://www.company.co.jp/

This is a continuous feature.

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Number of dotsSubdomains like 

http://www.my-bank.update.data.com.Redirection script, such as

http://www.google.com/url?q=http://www.badsite.com

This feature is simply the maximum number of dots (`.') contained in any of the links present in the email, and is a continuous feature.

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Contains javascriptAttackers can use JavaScript to hide

information from the user, and potentially launch sophisticated attacks.

An email is flagged with the “contains javascript” feature if the string “javascript” appears in the email, regardless of whether it is actually in a <script> or <a> tag

This is a binary feature.

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Spam-filter outputThis is a binary feature, using the trained

version of SpamAssassin with the default rule weights and threshold.

“Ham” or “Spam”This is a Binary feature.

Empirical Evaluation

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Machine-Learning Implementation Testing Spam Assassin Datasets Additional ChallengesFalse Positives vs. False Negatives

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Machine-Learning Implementation-PILFERFirst, run a set of scripts to extract all the

features listed.Second , we train and test a classifier using

10-fold cross validation. Random Forest (classifier)

Random forests create a number of decision trees and each decision tree is made by randomly choosing an attribute to split on at each level, and then pruning the tree.

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• we use a random forest as a classifier.

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Testing SpamAssassinSpamAssassin is a widely-deployed freely-

available spam filter that is highly accurate in classifying spam emails.

We classify the exact same dataset using SpamAssassin version 3.1.0, using the default thresholds and rules.

Using “Untrain” SpamAssassin “Training” on 10-fold

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DatasetsTwo publicly available datasets.

ham corpora from the SpamAssassin project6950 non-phishing non-spam emails

Phishingcorpusapproximately 860 email messages

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Additional ChallengesThe age of the dataset. Phishing websites are short-lived. Some of our features can therefore not be

extracted from older emails, making our tests difficult. EX: Domain linked to

Result

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Conclusion

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it is possible to detect phishing emails with high accuracy by using a specialized filter, using features that are more directly applicable to phishing emails than those employed by general purpose spam filters.

Reference

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I. Fette, N. Sadeh, and A. Tomasic. Learning to detect phishing emails. In Proceedings of the International World Wide Web Conference (WWW), pages 649–656, 2007.

www.ics.uci.edu/.../Learning%20to%20Detect%20Phishing%20Emails.pptx

http://armorize-cht.blogspot.com/2010/01/phishing-mail.html

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