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Vault Applications are used to store potentially sensitive information on a smartphone; and are available on Android and iOS. The purpose of using these applications could be used to hide potential evidence or illicit photos. After comparing five different iOS photo vaults, each vault left evidence and photos behind. However, of the three forensic toolkits used, each produced different results in their scans of the phone. The media left behind was due to the photo vaults not protecting their information as claimed, and using basic obfuscation techniques in place of security controls. Future research will look at how newer security controls are implemented, and if they are easily discoverable as well.

Research shows that 1 in 25 children experience a sexual solicitation online in which the offender asks to meet with them in the physical world. Investigating internet crimes against children is complicated because law enforcement agencies are overwhelmed by the sheer volume of cases, with an astonishing 10.2 million reports of child sexual exploitation to the CyberTipline in 2017 alone, as well as the data within each case that needs to be processed. Chats between minors and offenders can last for weeks or even months, leading to thousands of lines of text that law enforcement must process quickly, for multiple victims, and on multiple messaging platforms. However, unlike humans, machines can process an enormous amount of data quickly. This talk will outline the mindset of the internet predator and why it is difficult to investigate child sexual solicitations, then highlight how natural language processing methodologies may assist law enforcement with prioritizing cases involving crimes against children.

Cyberbullying and internet trolling are both forms of online aggression or cyberharassment; however. research has yet to assess the prevalence of these behaviors in relationship to one another. In addition, the current study was the first to investigate whether individual differences and self-esteeem discerned between self reported cyberbullies and/or internet trolls ( i.e. Never engaged in either, Cyberbully-only, Troll-Only, Both Cyberbully and Troll). Of 308 respondents solicited from Mechanical Turk, 70 engaged in cyberbullying behaviors, 20 engaged in onlt trolling behaviors, 129 self-reported both behaviors, and 89 self-reported neither behavior. Results yielded low self-esteem, low conscientiousness, and low internal moral values for both cyberbullying and trolling behaviors. However, there were differentiating factors between individuals who only engaged in cyberbullyinh behaviors (high on neuroticism) vs. trolling-only behaviors (high on openness to experience). Individuals who engaged in both behaviors scored higher on extraversion, lower on agreeableness, and lower on self-esteem compared to individuals who engaged in neither behavior.

Cyberbullying and internet trolling are both forms of online aggression or cyberharassment; however. research has yet to assess the prevalence of these behaviors in relationship to one another. In addition, the current study was the first to investigate whether individual differences and self-esteeem discerned between self reported cyberbullies and/or internet trolls ( i.e. Never engaged in either, Cyberbully-only, Troll-Only, Both Cyberbully and Troll). Of 308 respondents solicited from Mechanical Turk, 70 engaged in cyberbullying behaviors, 20 engaged in onlt trolling behaviors, 129 self-reported both behaviors, and 89 self-reported neither behavior. Results yielded low self-esteem, low conscientiousness, and low internal moral values for both cyberbullying and trolling behaviors. However, there were differentiating factors between individuals who only engaged in cyberbullyinh behaviors (high on neuroticism) vs. trolling-only behaviors (high on openness to experience). Individuals who engaged in both behaviors scored higher on extraversion, lower on agreeableness, and lower on self-esteem compared to individuals who engaged in neither behavior.

Cyberbullying and internet trolling are both forms of online aggression or cyberharassment; however, research has yet to assess the prevalence of these behaviors in relationship to one another. In addition, the current study was the first to investigate whether individual differences and self-esteem discerned between self-reported cyberbullies and/or internet trolls (i.e., Never engaged in either, Cyberbully-only, Troll-only, Both Cyberbully and Troll). Of 308 respondents solicited from Mechanical Turk, 70 engaged in cyberbullying behaviors, 20 engaged in only trolling behaviors, 129 self-reported both behaviors, and 89 self-reported neither behavior. Results yielded low self-esteem, low conscientiousness, and low internal moral values for both cyberbullying and trolling behaviors. However, there were differentiating factors between individuals who only engaged in cyberbu lying behaviors (high on neuroticism) vs. trolling-only behaviors (high on openness to experience). Individuals who engaged in both behaviors scored higher on extraversion, lower on agreeableness, and lower on self-esteem compared to individuals who engaged in neither behavior.

Not too long ago, trolls only existed in folklore as supernatural beings described as grotesque and ill-mannered and often dwelling under bridges in fairy tales. However, trolls are no longer a creature of myth that is scarcely seen or heard from; instead, they have abandoned bridges for a new home – the Internet. This talk will introduce the audience to the Internet Troll – a subculture of the Internet that is “turning the web into a cesspool of aggression and violence” (Stein, 2016).

Ashley Madison is an online dating website for individuals who are married or in a committed relationship and are interested in having an affair; their slogan is “Life is short – Have an affair.” In August 2015, the Ashley Madison website was hacked and customer information was publically posted on the Internet for the world to see. Journalists, researchers and bloggers began downloading and analyzing the breached data, but for some, ethical concerns were raised. By viewing the Ashley Madison dataset, are we legitimizing the manner in which the dataset was obtained, and are we as a society encouraging future data exploits?

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

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

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