TY - GEN
T1 - Online social network information forensics
T2 - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
AU - Umair, Amber
AU - Nanda, Priyadarsi
AU - He, Xiangjian
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - Online Social Networks (OSN) such as Facebook, Twitter, LinkedIn, and Instagram are heavily used to socialize, entertain or gain insights on people behavior and their activities. Everyday terabytes of data is generated over these networks, which is then used by the businesses to generate revenue or misused by the wrongdoers to exploit vulnerabilities of these social network platforms. Specifically social network information helps in extracting various important features such as; user association, access pattern, location information etc. Recent research shows, many such features could be used to develop novel attack models and investigate further into defending the users from exposing their information to outsiders. This paper analyzes some of the available tools to extract OSN information and discusses research work on similar type of unstructured data. Recent research works, which focus on gathering bits and pieces of information to extract meaningful results for digital forensics, has been discussed. An online survey is conducted to gauge the cautiousness of users in social media usage in terms of personal information dissemination.
AB - Online Social Networks (OSN) such as Facebook, Twitter, LinkedIn, and Instagram are heavily used to socialize, entertain or gain insights on people behavior and their activities. Everyday terabytes of data is generated over these networks, which is then used by the businesses to generate revenue or misused by the wrongdoers to exploit vulnerabilities of these social network platforms. Specifically social network information helps in extracting various important features such as; user association, access pattern, location information etc. Recent research shows, many such features could be used to develop novel attack models and investigate further into defending the users from exposing their information to outsiders. This paper analyzes some of the available tools to extract OSN information and discusses research work on similar type of unstructured data. Recent research works, which focus on gathering bits and pieces of information to extract meaningful results for digital forensics, has been discussed. An online survey is conducted to gauge the cautiousness of users in social media usage in terms of personal information dissemination.
KW - Digital Forensics
KW - Facebook
KW - Online Social networks
KW - Social snapshot
UR - http://www.scopus.com/inward/record.url?scp=85032351383&partnerID=8YFLogxK
U2 - 10.1109/Trustcom/BigDataSE/ICESS.2017.364
DO - 10.1109/Trustcom/BigDataSE/ICESS.2017.364
M3 - Conference contribution
AN - SCOPUS:85032351383
T3 - Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
SP - 1139
EP - 1144
BT - Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 August 2017 through 4 August 2017
ER -
