| Description |
1 online resource (1 volume) : illustrations |
| Bibliography |
Includes bibliographical references. |
| Contents |
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. |
|
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. |
|
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. |
|
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. |
|
6.2.3 -- Design Ensemble Method6.3 -- Research implication; 6.4 -- Recommendations for future research; 6.5 -- Closing note; References. |
| Summary |
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. |
| Subject |
Phishing.
|
|
Computer networks -- Security measures.
|
|
Hameçonnage. |
|
Réseaux d'ordinateurs -- Sécurité -- Mesures. |
|
Computer networks -- Security measures |
|
Phishing |
| Added Author |
Amiri, Iraj Sadegh, 1977- author.
|
|
Fazeldehkordi, Elahe, author.
|
| Other Form: |
Print version: Akanbi, Oluwatobi Ayodeji. Machine learning approach to phishing detection and defense. Waltham, MA : Syngress, [2015] 0128029277 9780128029275 (OCoLC)898228281 |
| ISBN |
9780128029466 |
|
0128029463 |
|
More Resources