Risk analysis in facebook based on user anomalous behaviors

Communication technology has completely occupied all the areas of applications. Last decade has however witnessed a drastic evolution in information and communication technology due to the introduction of social media network. Business growth is further achieved via these social media. Nevertheless, increase in the usage of online social networks (OSN) such as Face book, twitter, Instagram etc has however led to the increase in privacy and security concerns. Due to such risk parameters, it is always a best practice to have a mechanism to assign a risk score to each online social network user. This paper therefore put forth risk analysis of Face book. The main objective of this work is to identify risk occurrence whenever more user behavior shows deviations from their normal behavior while operating through Face book. Each user's behavior is identified based on the behavioral features such as comment ratio, friendship ratio, post ratio and comment feedback ratio. Further, if ratio of behavioral features exceeds the threshold value then the respective user is treated to be of risky in behavior. This paper presents experimental analysis carried out for the aforementioned objective. This work hence acts as a risk indicator to the administrator of the Face book services so that they can formulate strategies to overcome the same.

[1]  Leyla Bilge,et al.  All your contacts are belong to us: automated identity theft attacks on social networks , 2009, WWW '09.

[2]  Michalis Faloutsos,et al.  An analysis of socware cascades in online social networks , 2013, WWW.

[3]  Gianluca Stringhini,et al.  COMPA: Detecting Compromised Accounts on Social Networks , 2013, NDSS.

[4]  Konstantin Beznosov,et al.  Graph-based Sybil Detection in social and information systems , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[5]  Konstantin Beznosov,et al.  Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs , 2015, NDSS.

[6]  Michalis Faloutsos,et al.  Efficient and Scalable Socware Detection in Online Social Networks , 2012, USENIX Security Symposium.

[7]  Michael Sirivianos,et al.  Aiding the Detection of Fake Accounts in Large Scale Social Online Services , 2012, NSDI.

[8]  Alessandro Acquisti,et al.  Information revelation and privacy in online social networks , 2005, WPES '05.

[9]  Ben Y. Zhao,et al.  Uncovering social network sybils in the wild , 2011, IMC '11.

[10]  Yuval Elovici,et al.  Online Social Networks: Threats and Solutions , 2013, IEEE Communications Surveys & Tutorials.

[11]  Phil McKenna.  The rise of cyberbullying , 2007 .

[12]  Krishna P. Gummadi,et al.  An analysis of social network-based Sybil defenses , 2010, SIGCOMM 2010.

[13]  Jun Hu,et al.  Detecting and characterizing social spam campaigns , 2010, IMC '10.

[14]  Hossein Saidi,et al.  Malware propagation in Online Social Networks , 2009, 2009 4th International Conference on Malicious and Unwanted Software (MALWARE).