A machine-learning approach to phishing detection and defense / Oluwatobi Ayodeji Akanbi, Iraj Sadegh Amiri, Elahe Fazeldehkordi.

Format:

Book

Author/Creator:

Akanbi, Oluwatobi Ayodeji, author.

Amiri, Iraj Sadegh, author.

Fazeldehkordi, Elahe, author.

Language:

English

Subjects (All):

Phishing.

Computer networks--Security measures.

Computer networks.

Physical Description:

1 online resource (101 p.)

Edition:

1st edition

Place of Publication:

Waltham, Massachusetts : Syngress, 2015.

Language Note:

English

System Details:

text file

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

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

Notes:

Description based upon print version of record.

Includes bibliographical references.

Description based on online resource; title from PDF title page (ebrary, viewed January 09, 2015).

ISBN:

9780128029466

0128029463

OCLC:

900882966