Overview
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Explore machine learning techniques for phishing detection in this 32-minute conference talk from BSidesLV 2019. Delve into common phishing attacks, manual detection efforts, and behavioral metrics before discovering a more effective approach using data sources, textual and visual analysis, and email and website classification. Learn about phishing datasets, repositories, and tools for data collection, as well as URL extraction techniques. Examine deployment strategies, content reputation, and statistically significant coefficients in modeling. Gain insights into assumptions, future work, and conclusions in the field of phishing detection, followed by a Q&A session and model discussion.
Syllabus
Introduction
Who am I
What is Phishing
Agenda
Motivation
Common phishing attacks
Manual effort
Humandriven detection
Behavioral metrics
Better approach
Data sources
Textual and visual analysis
Email classification
Website classification
Data collection
Phishing datasets
Phishing repositories tools
Phishing website datasets
What is URL extraction
Deployment
Content
Reputation
Most statistically significant coefficients
Modeling
Assumptions
Summary
Future work
Conclusions
Questions
Models
Discussion
Taught by
BSidesLV