A machine learning approach to phishing detection and defense /

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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 Det...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Akanbi, Oluwatobi Ayodeji (Autor), Amiri, Iraj Sadegh, 1977- (Autor), Fazeldehkordi, Elahe (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Waltham, MA : Syngress, [2015]
Temas:
Phishing.
Computer networks > Security measures.
Hameçonnage.
Réseaux d'ordinateurs > Sécurité > Mesures.
Computer networks > Security measures
Phishing
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.

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