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Using Machine Learning to Detect Mal...

Northern Kentucky University.

Using Machine Learning to Detect Malicious Websites.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Using Machine Learning to Detect Malicious Websites./
Author:	Elsaleh, Rasheed.
Description:	1 online resource (45 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780438131972

Using Machine Learning to Detect Malicious Websites.
Elsaleh, Rasheed.

Using Machine Learning to Detect Malicious Websites. - 1 online resource (45 pages)

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--Northern Kentucky University, 2018.

Includes bibliographical references

Malicious websites are a rich platform for many online attacks. These websites threaten the privacy and security of users, businesses, governments, and organizations every day. Malicious websites usually attempt to steal information or serve malware. Today's most popular type of malicious websites is phishing, which lure users into giving away sensitive information.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438131972Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Using Machine Learning to Detect Malicious Websites.
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020 $a 9780438131972
035 $a (MiAaPQ)AAI10686812
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035 $a AAI10686812
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Elsaleh, Rasheed. $3 1192640
245 1 0 $a Using Machine Learning to Detect Malicious Websites.
264 0 $c 2018
300 $a 1 online resource (45 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: James Walden.
502 $a Thesis (M.S.)--Northern Kentucky University, 2018.
504 $a Includes bibliographical references
520 $a Malicious websites are a rich platform for many online attacks. These websites threaten the privacy and security of users, businesses, governments, and organizations every day. Malicious websites usually attempt to steal information or serve malware. Today's most popular type of malicious websites is phishing, which lure users into giving away sensitive information.
520 $a To protect users from such websites, detection capabilities need to be developed. Current detection methods such as signature based heuristic approaches are limited to known attack patterns and fail to scale with the rapid evolution of malicious websites on the web. This study collects a custom dataset of verified malicious phishing sites and uses machine learning classification techniques to detect malicious URLs and pages. Results show high recall and precision confirming that using machine learning techniques for detecting malicious websites can further improve the protection of users on the web.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Northern Kentucky University. $b Computer Science. $3 1192641
773 0 $t Masters Abstracts International $g 57-06(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10686812 $z click for full text (PQDT)

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