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Table of Contents
About the Editor and Contributors; Contributors; Contributor Bios; Contents; An Overview of the Scam Problem ; About Scams and This Book; About This Book; References; 1 Scams and Targeting; 1.1 Yields and Targeting; 1.2 Understanding Yields and Trends; References; Part I Identifying Trends; 2 Identifying Scams and Trends; 2.1 Gathering Hundreds of Thousands of Scam Messages; 2.2 Taxonomy of Scam Emails; 2.2.1 Non-Targeted Scams; 2.2.2 Targeted Scams; 2.2.3 Scams that Are Both Non-targeted and Targeted; 2.2.4 Miscellaneous Scams; 2.3 Scam Classification; 2.4 Scam Trends.
2.4.1 Targeted vs. Non-Targeted Scams2.4.2 Scams on the Rise; 2.4.3 Scams in Decline; References; 3 Predicting Trends; 3.1 Vulnerabilities Point to Trends; 3.2 Measuring Credibility; References; Part II Why Do People Fall for Scams?; 4 Persuasion in Scams; 4.1 Persuasion in Emails; 4.2 Principles of Persuasion; 4.2.1 Principles of Persuasion in Scam Categories; 4.2.2 Scam Terms: Trends and Persuasion; 4.2.3 Comparison Between Scam and Legitimate Term Trends; References; Part III Filtering Technology; 5 Traditional Countermeasures to Unwanted Email; 5.1 The History of Spam.
5.2 Anti-Spam Landscape5.3 Content-Based Spam Filtering; 5.4 Blacklisting Approaches; 5.5 Anti-Spoofing Approaches; 5.5.1 DKIM; 5.5.2 SPF; 5.5.3 DMARC; References; 6 Obfuscation in Spam and Scam; 6.1 Confusable Characters and Homograph Scam Attacks; 6.2 How to Test the Attack; 6.3 Detecting Obfuscated Scam; References; 7 Semantic Analysis of Messages; 7.1 Example: Stranded Traveler Scams; 7.2 Detecting Storylines; 7.3 Detecting Brand Abuse; Part IV Understanding the Problem Starts with Measuring It; 8 Case Study: Sales Scams; 8.1 The Automated Honeypot Ad System; 8.1.1 Magnetic Honeypot Ads.
8.1.2 Automated Communication with Scammers8.2 Automated Scammer Interaction; 8.3 Where Are the Scammers?; 8.3.1 Collected Emails and Threads; 8.3.2 IP Addresses; 8.3.3 Email Accounts; 8.3.4 Shipping Addresses and Phone Numbers; 8.3.5 Attribution: Performing Scammer Group Classification; 8.4 Discussion; 9 Case Study: Rental Scams; 9.1 Dataset; 9.1.1 Rental Listing Crawling; 9.1.2 Campaign Identification; 9.1.3 Campaign Expansion Phase: Latitudinal; 9.1.4 Campaign Expansion Phase: Longitudinal; 9.1.5 Campaign Summaries; 9.2 Credit Report Rental Scams; 9.2.1 Data Collection.
9.2.2 Dataset Sanity Check9.2.3 Two-Scams-in-One; 9.2.4 In-Depth Analysis; 9.3 Clone Scams; 9.3.1 Data Collection; 9.3.2 In-Depth Analysis of Confirmed Scams; 9.4 Realtor Service Scams; 9.4.1 Data Collection; 9.4.2 American Standard Online; 9.4.3 New Line Equity; 9.4.4 Search Rent to Own; 9.5 Flagged Ad Analysis; References; 10 Case Study: Romance Scams; 10.1 Romance Scams: A Hurtful Crime; 10.2 Collecting Intelligence; 10.3 Romance Scam Taxonomy; 10.3.1 Traditional Romance Scam; 10.3.2 Affiliate Marketing Scam; 10.3.3 Phone Scam; 10.3.4 Simulated Spam Filter Results; 10.4 Filtering Insights.
2.4.1 Targeted vs. Non-Targeted Scams2.4.2 Scams on the Rise; 2.4.3 Scams in Decline; References; 3 Predicting Trends; 3.1 Vulnerabilities Point to Trends; 3.2 Measuring Credibility; References; Part II Why Do People Fall for Scams?; 4 Persuasion in Scams; 4.1 Persuasion in Emails; 4.2 Principles of Persuasion; 4.2.1 Principles of Persuasion in Scam Categories; 4.2.2 Scam Terms: Trends and Persuasion; 4.2.3 Comparison Between Scam and Legitimate Term Trends; References; Part III Filtering Technology; 5 Traditional Countermeasures to Unwanted Email; 5.1 The History of Spam.
5.2 Anti-Spam Landscape5.3 Content-Based Spam Filtering; 5.4 Blacklisting Approaches; 5.5 Anti-Spoofing Approaches; 5.5.1 DKIM; 5.5.2 SPF; 5.5.3 DMARC; References; 6 Obfuscation in Spam and Scam; 6.1 Confusable Characters and Homograph Scam Attacks; 6.2 How to Test the Attack; 6.3 Detecting Obfuscated Scam; References; 7 Semantic Analysis of Messages; 7.1 Example: Stranded Traveler Scams; 7.2 Detecting Storylines; 7.3 Detecting Brand Abuse; Part IV Understanding the Problem Starts with Measuring It; 8 Case Study: Sales Scams; 8.1 The Automated Honeypot Ad System; 8.1.1 Magnetic Honeypot Ads.
8.1.2 Automated Communication with Scammers8.2 Automated Scammer Interaction; 8.3 Where Are the Scammers?; 8.3.1 Collected Emails and Threads; 8.3.2 IP Addresses; 8.3.3 Email Accounts; 8.3.4 Shipping Addresses and Phone Numbers; 8.3.5 Attribution: Performing Scammer Group Classification; 8.4 Discussion; 9 Case Study: Rental Scams; 9.1 Dataset; 9.1.1 Rental Listing Crawling; 9.1.2 Campaign Identification; 9.1.3 Campaign Expansion Phase: Latitudinal; 9.1.4 Campaign Expansion Phase: Longitudinal; 9.1.5 Campaign Summaries; 9.2 Credit Report Rental Scams; 9.2.1 Data Collection.
9.2.2 Dataset Sanity Check9.2.3 Two-Scams-in-One; 9.2.4 In-Depth Analysis; 9.3 Clone Scams; 9.3.1 Data Collection; 9.3.2 In-Depth Analysis of Confirmed Scams; 9.4 Realtor Service Scams; 9.4.1 Data Collection; 9.4.2 American Standard Online; 9.4.3 New Line Equity; 9.4.4 Search Rent to Own; 9.5 Flagged Ad Analysis; References; 10 Case Study: Romance Scams; 10.1 Romance Scams: A Hurtful Crime; 10.2 Collecting Intelligence; 10.3 Romance Scam Taxonomy; 10.3.1 Traditional Romance Scam; 10.3.2 Affiliate Marketing Scam; 10.3.3 Phone Scam; 10.3.4 Simulated Spam Filter Results; 10.4 Filtering Insights.