Proposal / Submission Type

Peer Reviewed Paper

Location

Richmond, Virginia

Start Date

28-5-2014 1:00 PM

Abstract

Digital evidence plays a crucial role in child pornography investigations. However, in the following case study, the authors argue that the behavioral analysis or “profiling” of digital evidence can also play a vital role in child pornography investigations. The following case study assessed the Internet Browsing History (Internet Explorer Bookmarks, Mozilla Bookmarks, and Mozilla History) from a suspected child pornography user’s computer. The suspect in this case claimed to be conducting an ad hoc law enforcement investigation. After the URLs were classified (Neutral; Adult Porn; Child Porn; Adult Dating sites; Pictures from Social Networking Profiles; Chat Sessions; Bestiality; Data Cleaning; Gay Porn), the Internet history files were statistically analyzed to determine prevalence and trends in Internet browsing. First, a frequency analysis was used to determine a baseline of online behavior. Results showed 54% (n = 3205) of the URLs were classified as “neutral” and 38.8% (n = 2265) of the URLs were classified as a porn website. Only 10.8% of the URLs were classified as child pornography websites. However when the IE history file was analyzed by visit, or “hit,” count, the Pictures/Profiles (31.5%) category had the highest visit count followed by Neutral (19.3%), Gay Porn (17%), and Child Porn (16.6%). When comparing the frequency of URLs to the Hit Count for each pornography type, it was noted that the accused was accessing gay porn, child porn, chat rooms, and picture profiles (i.e., from Facebook) more often than adult porn and neutral websites. The authors concluded that the suspect in this case was in fact a child pornography user and not an ad hoc investigator, and the findings from the behavioral analysis were admitted as evidence in the sentencing hearing for this case. The authors believe this case study illustrates the ability to conduct a behavioral analysis of digital evidence. More work is required to further validate the behavioral analysis process described, but the ability to infer the predilection for being a consumer of child pornography based on Internet artifacts may prove to be a powerful tool for investigators.

Keywords: Internet child pornography, digital forensics, computer crime investigation, Internet artifacts, profiling, behavioral analysis

 
May 28th, 1:00 PM

Using Internet Artifacts to Profile a Child Pornography Suspect

Richmond, Virginia

Digital evidence plays a crucial role in child pornography investigations. However, in the following case study, the authors argue that the behavioral analysis or “profiling” of digital evidence can also play a vital role in child pornography investigations. The following case study assessed the Internet Browsing History (Internet Explorer Bookmarks, Mozilla Bookmarks, and Mozilla History) from a suspected child pornography user’s computer. The suspect in this case claimed to be conducting an ad hoc law enforcement investigation. After the URLs were classified (Neutral; Adult Porn; Child Porn; Adult Dating sites; Pictures from Social Networking Profiles; Chat Sessions; Bestiality; Data Cleaning; Gay Porn), the Internet history files were statistically analyzed to determine prevalence and trends in Internet browsing. First, a frequency analysis was used to determine a baseline of online behavior. Results showed 54% (n = 3205) of the URLs were classified as “neutral” and 38.8% (n = 2265) of the URLs were classified as a porn website. Only 10.8% of the URLs were classified as child pornography websites. However when the IE history file was analyzed by visit, or “hit,” count, the Pictures/Profiles (31.5%) category had the highest visit count followed by Neutral (19.3%), Gay Porn (17%), and Child Porn (16.6%). When comparing the frequency of URLs to the Hit Count for each pornography type, it was noted that the accused was accessing gay porn, child porn, chat rooms, and picture profiles (i.e., from Facebook) more often than adult porn and neutral websites. The authors concluded that the suspect in this case was in fact a child pornography user and not an ad hoc investigator, and the findings from the behavioral analysis were admitted as evidence in the sentencing hearing for this case. The authors believe this case study illustrates the ability to conduct a behavioral analysis of digital evidence. More work is required to further validate the behavioral analysis process described, but the ability to infer the predilection for being a consumer of child pornography based on Internet artifacts may prove to be a powerful tool for investigators.

Keywords: Internet child pornography, digital forensics, computer crime investigation, Internet artifacts, profiling, behavioral analysis