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A machine learning approach to phishing detection and defense /
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 Det...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Syngress,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 008 | 150128s2015 maua ob 000 0 eng d | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Akanbi, Oluwatobi Ayodeji, |e author. | |
| 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] | |
| 264 | 4 | |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. | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed Janurary 21, 2014). | |
| 505 | 0 | |a Cover; Title Page; Copyright Page; Contents; Abstract; List of Tables; List of figures; List of abbreviation; Chapter 1 -- Introduction; 1.1 -- Introduction; 1.2 -- Problem background; 1.3 -- Problem statement; 1.4 -- Purpose of study; 1.5 -- Project objectives; 1.6 -- Scope of study; 1.7 -- The significance of study; 1.8 -- Organization of report; Chapter 2 -- Literature Review; 2.1 -- Introduction; 2.2 -- Phishing; 2.3 -- Existing anti-phishing approaches; 2.3.1 -- Non-Content-Based Approaches; 2.3.2 -- Content-Based Approaches; 2.3.3 -- Visual Similarity-Based Approach. | |
| 505 | 8 | |a 2.3.4 -- Character-Based Approach2.4 -- Existing techniques; 2.4.1 -- Attribute-Based Anti-Phishing Technique; 2.4.2 -- Generic Algorithm-Based Anti-Phishing Technique; 2.4.3 -- An Identity-Based Anti-Phishing Techniques; 2.5 -- Design of classifiers; 2.5.1 -- Hybrid System; 2.5.2 -- Lookup System; 2.5.3 -- Classifier System; 2.5.4 -- Ensemble System ; 2.5.4.1 -- Simple Majority Vote; 2.6 -- Normalization; 2.7 -- Related work; 2.8 -- Summary; Chapter 3 -- Research Methodology; 3.1 -- Introduction; 3.2 -- Research framework; 3.3 -- Research design. | |
| 505 | 8 | |a 3.3.1 -- Phase 1: Dataset Processing and Feature Extraction3.3.2 -- Phase 2: Evaluation of Individual Classifier; 3.3.2.1 -- Classification Background; 3.3.2.2 -- Classifier Performance; 3.3.2.2.1 -- C5.0 Algorithm; 3.3.2.2.2 -- K-Nearest Neighbour; 3.3.2.2.3 -- Support Vector Machine (SVM); 3.3.2.2.4 -- Linear Regression; 3.3.3 -- Phase 3a: Evaluation of Classifier Ensemble; 3.3.4 -- Phase 3b: Comparison of Individual versus Ensemble Technique; 3.4 -- Dataset; 3.4.1 -- Phishtank; 3.5 -- Summary; Chapter 4 -- Feature Extraction; 4.1 -- Introduction; 4.2 -- Dataset processing. | |
| 505 | 8 | |a 4.2.1 -- Feature Extraction4.2.2 -- Extracted Features; 4.2.3 -- Data Verification; 4.2.4 -- Data Normalization; 4.3 -- Dataset division; 4.4 -- Summary; Chapter 5 -- Implementation and Result; 5.1 -- Introduction; 5.2 -- An overview of the investigation; 5.2.1 -- Experimental Setup; 5.3 -- Training and testing model (baseline model); 5.4 -- Ensemble design and voting scheme; 5.5 -- Comparative study; 5.6 -- Summary; Chapter 6 -- Conclusions; 6.1 -- Concluding remarks; 6.2 -- Research contribution; 6.2.1 -- Dataset Preprocessing Technique; 6.2.2 -- Validation Technique. | |
| 505 | 8 | |a 6.2.3 -- Design Ensemble Method6.3 -- Research implication; 6.4 -- Recommendations for future research; 6.5 -- Closing note; 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Phishing. | |
| 650 | 0 | |a Computer networks |x Security measures. | |
| 650 | 6 | |a Hameçonnage. | |
| 650 | 6 | |a Réseaux d'ordinateurs |x Sécurité |x Mesures. | |
| 650 | 7 | |a Computer networks |x Security measures |2 fast | |
| 650 | 7 | |a Phishing |2 fast | |
| 700 | 1 | |a Amiri, Iraj Sadegh, |d 1977- |e author. | |
| 700 | 1 | |a Fazeldehkordi, Elahe, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Akanbi, Oluwatobi Ayodeji. |t Machine learning approach to phishing detection and defense. |d Waltham, MA : Syngress, [2015] |z 0128029277 |z 9780128029275 |w (OCoLC)898228281 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780128029275/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a YBP Library Services |b YANK |n 12216967 | ||
| 994 | |a 92 |b IZTAP | ||