000			02663cam a2200421 a 4500
001			ocn898326414
003			OCoLC
005			20250612155430.0
006			m o d
007			cr |n|||||||||
008			141219t20142015ne ob 000 0 eng d
040			_aIDEBK _beng _epn _cIDEBK _dN$T _dEBLCP _dN$T _dOPELS _dOCLCF _dDEBSZ _dDEBBG _dOCLCQ _dMERUC _dOCLCQ _dU3W _dD6H _dOCLCQ _dCUY _dZCU _dICG _dDKC _dOCLCQ _dS2H _dOCLCO _dOCLCQ _dOCLCO _dOCLCL _dSXB _dOCLCQ _dUKKRT
020			_a1322480850 _q(electronic bk.)
020			_a9781322480855 _q(electronic bk.)
020			_a9780128029466 _q(electronic bk.)
020			_a0128029463 _q(electronic bk.)
020			_z9780128029275
035			_a(OCoLC)898326414
050		4	_aHV6773.15.P45
072		7	_aSOC _x004000 _2bisacsh
082	0	4	_a364.168 _223
100	1		_aAkanbi, Oluwatobi Ayodeji, _eauthor. _933775
245	1	2	_aA machine-learning approach to phishing detection and defense / _cOluwatobi Ayodeji Akanbi, Iraj Sadegh Amiri, Elahe Fazeldehkordi.
260			_aAmsterdam : _bElsevier, _c[2014]
264		4	_c�2015
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aPhishing 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 organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats.
504			_aIncludes bibliographical references.
650		0	_aPhishing. _933776
650		0	_aComputer networks _xSecurity measures. _911925
700	1		_aAmiri, Iraj Sadegh, _d1977- _eauthor. _1https://id.oclc.org/worldcat/entity/E39PCjvdBxR7D43j3qrmGqTkcP _933777
700	1		_aFazeldehkordi, Elahe, _eauthor. _933778
776	0	8	_iErscheint auch als: _nDruck-Ausgabe _tAmiri, I.S.A Machine-Learning Approach to Phishing Detection and Defense
856	4	0	_3ScienceDirect _uhttps://www.sciencedirect.com/science/book/9780128029275
999			_c216330 _d216330