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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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A cloud-based forensics tracking scheme for online social network clients.

Feng-Yu Lin1, Chien-Cheng Huang2, Pei-Ying Chang3

  • 1Department of Criminal Investigation, Central Police University, No. 56, Shujen Road, Takang Village, Kueishan District, Taoyuan City 33304, Taiwan, ROC.

Forensic Science International
|September 6, 2015
PubMed
Summary
This summary is machine-generated.

Law Enforcement Agencies can now trace criminal suspects' physical locations and probable identities on online social networks (OSNs). This novel forensics tracking method utilizes IP location and network forensics for digital investigations.

Keywords:
IP locationLaw Enforcement AgencyNetwork forensicsOnline social network

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Area of Science:

  • Digital Forensics
  • Cybersecurity
  • Network Analysis

Background:

  • The increasing use of cloud-based applications, particularly online social networks (OSNs), presents challenges for law enforcement investigations.
  • Criminals exploit the external hosting of OSNs to impede digital investigations and intelligence gathering.
  • Identifying suspects' actual identities and geolocations on social media is a significant hurdle for current digital forensics.

Purpose of the Study:

  • To propose a novel scheme for forensics tracking on online social networks (OSNs).
  • To enable Law Enforcement Agencies (LEAs) to identify "actual" suspect identities and geolocations within virtual environments.
  • To develop an individualized location method and architecture for OSNs.

Main Methods:

  • The proposed scheme is based on the concepts of IP geolocation and network forensics.
  • It involves developing a forensics tracking mechanism specifically for OSNs.
  • Empirical analysis was conducted to evaluate the effectiveness of the proposed method.

Main Results:

  • The mechanism can instantly trace the "physical location" of a targeted service resource identifier (SRI) on OSNs.
  • It can associatively analyze the probable "identity" of the target client.
  • The system is effective when clients are using popular OSN applications like Facebook and Twitter.

Conclusions:

  • This research presents the first individualized location method and architecture developed and evaluated for OSNs.
  • The proposed scheme offers a significant advancement in digital investigation capabilities for LEAs.
  • Effective identification and geolocation of suspects on OSNs are now more achievable.