Authors: Sophia Alim, Ruqayya Abdulrahman, Daniel Neagu, Mick Ridley
Addresses: AI Research Centre, University of Bradford, Bradford BD7 1DP, UK. ' AI Research Centre, University of Bradford, Bradford BD7 1DP, UK. ' AI Research Centre, University of Bradford, Bradford BD7 1DP, UK. ' AI Research Centre, University of Bradford, Bradford BD7 1DP, UK
Abstract: The increase in social computing has provided the situation where large amounts of personal information are being posted online. This makes people vulnerable to social engineering attacks because their personal details are readily available. Our automated approach for personal data extraction was developed to extract personal details and top friends from MySpace profiles and place them into a repository. An online social network graph was generated from the repository data where nodes represent peoples| profiles. Analysis was carried out into what factors affect node vulnerability. The graph analysis identified structural features of the nodes, e.g., clustering coefficient, indegree and outdegree, which contribute towards vulnerability. From this, it was found that the number of neighbours and the clustering coefficient were major factors in making a node vulnerable because of the potential to spread personal details around the network. These results provide a good foundation for future work on online vulnerability in online social networks (OSNs).
Keywords: online social networking; OSN; vulnerability analysis; information disclosure; automated data retrieval; profile data extraction; social networks; personal information; personal details; node vulnerability.
International Journal of Internet Technology and Secured Transactions, 2011 Vol.3 No.2, pp.194 - 209
Available online: 19 Apr 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article