A Machine-Learning Approach to Phishing Detection and Defense
| Other author | Amiri, I. S. |
| Other author | Akanbi, O. A. |
| Other author | Fazeldehkordi, E. |
| Format | Electronic |
| Publication Info | Syngress Press [Imprint] San Diego : Elsevier Science & Technology Books |
| Description | 100 p. 22.900 x 015.200 cm. |
| Supplemental Content | Full text available from eBook - Computer Science 2015 [EBCCS15] |
| Summary | Annotation 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 attacksHelp your business or organization avoid costly damage from phishing sourcesGain insight into machine-learning strategies for facinga variety of information security threats" |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| ISBN | 9780128029275 |
| ISBN | 0128029277 (Trade Paper) Active Record |
| Standard identifier# | 9780128029275 |
| Stock number | 00991439 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |