A Machine-Learning Approach to Phishing Detection and Defense (ePub)
(Sprache: Englisch)
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...
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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 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.
- Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks
- Help your business or organization avoid costly damage from phishing sources
- Gain insight into machine-learning strategies for facing a variety of information security threats
Autoren-Porträt von O. A. Akanbi, Iraj Sadegh Amiri, E. Fazeldehkordi
O.A. Akanbi received his B. Sc. (Hons, Information Technology - Software Engineering) from Kuala Lumpur Metropolitan University, Malaysia, M. Sc. in Information Security from University Teknologi Malaysia (UTM), and he is presently a graduate student in Computer Science at Texas Tech University His area of research is in CyberSecurity.
Bibliographische Angaben
- Autoren: O. A. Akanbi , Iraj Sadegh Amiri , E. Fazeldehkordi
- 2014, 100 Seiten, Englisch
- Verlag: Elsevier Science & Techn.
- ISBN-10: 0128029463
- ISBN-13: 9780128029466
- Erscheinungsdatum: 05.12.2014
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eBook Informationen
- Dateiformat: ePub
- Größe: 4.37 MB
- Mit Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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