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Produktbild: Phishing Detection Using Content-Based Image Classification
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Phishing Detection Using Content-Based Image Classification

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

02.06.2022

Abbildungen

35 SW-Abb., 3 SW-Fotos, 32 SW-Zeichn., 16 Tabellen

Verlag

Taylor & Francis

Seitenzahl

134

Maße (L/B/H)

22,2/14,5/1,1 cm

Gewicht

309 g

Sprache

Englisch

ISBN

978-1-03-210853-7

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

02.06.2022

Abbildungen

35 SW-Abb., 3 SW-Fotos, 32 SW-Zeichn., 16 Tabellen

Verlag

Taylor & Francis

Seitenzahl

134

Maße (L/B/H)

22,2/14,5/1,1 cm

Gewicht

309 g

Sprache

Englisch

ISBN

978-1-03-210853-7

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Phishing Detection Using Content-Based Image Classification

  • Chapter 1. Phishing and Cybersecurity. Basics of Phishing in Cybersecurity. Phishing Detection Techniques. List (whitelist/blacklist) based. Heuristics (pre-defined rules) based. Visual similarity based. Race between Phishers and Anti-Phishers. Chapter 2. Image Processing based Phishing Detection Techniques. Image processing based phishing detection techniques. Challenges in Phishing Detection using website images. Comparison of Techniques. Summary of Phishing detection using image processing techniques. Chapter 3. Implementing CNN for classifying phishing websites. Data Selection and Pre-Processing. Classification using CNN. CNN implementation. Performance metrics. Building a Convolutional Neural Network Model. Chapter 4. Transfer Learning Approach in Phishing Detection. Classification using Transfer Learning. Transfer Learning python implementation. Performance assessment of CNN models. Chapter 5. Feature Extraction and Representation Learning. Classification using Representation Learning. Data Preparation.. Feature Extraction using CNN off-the-shelf architectures. Handling class imbalance using SMOTE. SMOTE python implementation. Machine learning Classifier. Performance assessment of various experimentations. Chapter 6. Dimensionality Reduction Techniques. Basics of dimensionality reduction. PCA implementation using python. Performance assessment of various experimentations. Chapter 7. Feature Fusion Techniques. Basics of feature fusion technique. Different combinations of image representations. Different feature fusion approaches. Performance assessment of various experimentations. Chapter 8. Comparison of Phishing detection approaches. Classification Approaches. Evaluation of Classification Experiments. Comparison of the best performing model with the State-of-the-art. Chapter 9. Basics of Digital Image Processing. Basics of digital image processing. Basics of extracting features using OpenCV.