1. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Unit 4 Auction Fraud & Child Pornography The use of the Internet in an online transaction between buyer and seller the failure to deliver or pay is fraud. The illicit sex trade has thrived for years and has been exasperated by the Internet. Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski

2. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Internet Auction Fraud How does fraud occur?

3. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Common Forms of Auction Fraud Has anyone heard of these types of Auction Frauds ? Bid siphoning Shill bidding Bid shielding

4. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Bid Siphoning Con artists lure bidders off legitimate auction sites by offering to sell the “same” item at a lower price. Their intent is to trick consumers into sending money without proffering the item. By going off-site, buyers lose any protections the original site may provide, such as insurance, feedback forms, or guarantees.

5. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Shill Bidding  When fraudulent sellers or their “shills” bid on “sellers” items to drive up the price. This is also sometimes referred to as “Phantom Bidding.”

6. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Bid Shielding When fraudulent buyers submit very high bids to discourage other bidders from competing for the same item and then retract those bids so that people they know can get the item at a lower price.

7. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Auction Fraud Suspect Profile 75% male 25% female Most likely residing in California, New York, Florida, Texas, Illinois, or Ohio Average loss between $252.50 and $285.00 Average victim age 38.6 years

8. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Auction Fraud Prevention Be familiar with auction site Know available recourse action offered Examine seller feedback Know where the seller is located Note any warranties or return policies Avoid overpriced shipping costs

9. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Auction Fraud Investigation What is the name of the Web Site and/or web address (Uniform Resource Locator — URL) of the site that the purchase was made from? Did you save a copy of the screen that documented the transaction? Did you save any email in connection with the transaction? Did you save any electronic payment receipts in connection with the transaction?

10. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Child Pornography Computers have become the most common way for child pornographers to share their illegal images and lure children. There are many definitions of “child pornography.” “A photographic film or other visual representation made by electronic, mechanical, or other means that depicts a child less than 18 years old engaged in or depicted to be engaged in explicit sexual activity.”

11. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Child Pornography, Con’t Simple cut-and-paste commands have made victimizing and sharing illegal images too simple. Multiple images can be created in seconds and shared via instant messaging, file transfer, or email. The Child Pornography Prevention Act (1996) attempted to curb this.

12. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Child Pornography Prevention Act Would have outlawed computer-generated images of minors engaged in sexual situations. Since such images, the court reasoned, do not victimize real children, they do not qualify as child pornography.

13. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Scope of the Problem Actual number of suspects arrested are difficult to measure because it’s not tracked by the UCRs. The new NIBRs system is designed to allow for tracking pornography and child exploitation arrests. Studies estimate that 20,000 images of child pornography are posted on the Internet each week.

14. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Scope of the Problem, Con’t Former Soviet Union is the capital of the child pornography trade. Unstable economy and high unemployment contribute to the problem. 20–30% of street children in Moscow are engaged in prostitution or production of pornographic material. Estimated that 100,000 child pornography sites exist on the Internet today.

15. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Child Pornography Law Elements Knowingly possesses an undeveloped film, photographic negative, motion picture, videotape, computer image, or other recording: –Of a child (a person under 18 years old), –Or, person he or she should have reasonably known was under 18 years old, –Engaged in sexually explicit conduct or a sexually explicit sex act

16. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski Lolita Images 18-year-old “models” that look much younger to satisfy the child porn perversion and are not illegal. The Department of Defense Cyber Crime Center, using the MD5 message digest, can help identify known child pornography images.

17. © 2008 Pearson Education, Inc. Prentice Hall Upper Saddle River, NJ 07458 Investigating High-Tech Crime By Michael Knetzger and Jeremy Muraski MD5 Message Digest An algorithm that creates a 128-bit “fingerprint” similar in appearance to an automobile Vehicle Identification Number (VIN), but much longer. Known images of child pornography are kept in this Department of Defense database, which can be searched via the MD5 message digest number that it creates.