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A machine learning approach to phish...

Akanbi, Oluwatobi Ayodeji,

A machine learning approach to phishing detection and defense /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A machine learning approach to phishing detection and defense // Oluwatobi Ayodeji Akanbi, Iraj Sadegh Amiri, Elahe Fazeldehkordi.
Author:	Akanbi, Oluwatobi Ayodeji,
other author:	Amiri, Iraj Sadegh,
Description:	1 online resource (1 volume) :illustrations :
Subject:	Phishing. -
Online resource:	https://www.sciencedirect.com/science/book/9780128029275
ISBN:	9780128029466

A machine learning approach to phishing detection and defense /
Akanbi, Oluwatobi Ayodeji,

A machine learning approach to phishing detection and defense /Oluwatobi Ayodeji Akanbi, Iraj Sadegh Amiri, Elahe Fazeldehkordi. - 1 online resource (1 volume) :illustrations

Includes bibliographical references.

Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organiza.

ISBN: 9780128029466Subjects--Topical Terms:

907111
Phishing.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: HV6773.15.P45

Dewey Class. No.: 364.1633

A machine learning approach to phishing detection and defense /
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020 $a 9780128029466 $q (electronic bk.)
020 $a 0128029463 $q (electronic bk.)
020 $a 9780128029275
020 $a 0128029277
035 $a (OCoLC)900882966
035 $a els19100038
040 $a UMI $b eng $e rda $e pn $c UMI $d DEBBG $d OCLCF $d YDXCP $d OCLCQ $d SUE $d CEF $d OCLCQ $d AU@ $d LOA $d G3B $d CNCEN $d S8J $d K6U $d COCUF $d CNNOR $d VT2
041 0 $a eng
050 4 $a HV6773.15.P45
082 0 4 $a 364.1633 $2 23
100 1 $a Akanbi, Oluwatobi Ayodeji, $e author. $3 1295111
245 1 2 $a A machine learning approach to phishing detection and defense / $c Oluwatobi Ayodeji Akanbi, Iraj Sadegh Amiri, Elahe Fazeldehkordi.
264 1 $a Waltham, MA : $b Syngress, $c 2015.
300 $a 1 online resource (1 volume) : $b illustrations
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
504 $a Includes bibliographical references.
520 $a Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organiza.
650 0 $a Phishing. $3 907111
650 0 $a Computer networks $x Security measures. $3 555385
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Amiri, Iraj Sadegh, $d 1977- $3 1125796
700 1 $a Fazeldehkordi, Elahe, $e author. $3 1295112
856 4 0 $u https://www.sciencedirect.com/science/book/9780128029275

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