Abstract—Although the dramatic increase in
OSN usage, there are still a lot of security and privacy concerns. In such a
scenario, it would be very beneficial to have a mechanism able to assign a risk
score to each OSN user. In this paper, we propose a risk assessment based on
the idea that the more a user behavior diverges from what it can be considered
as a ‘normal behavior’, the more it should be considered risky. In doing this,
we have takein into account that OSN population is really heterogeneous in
observed behaviors. As such, it is not possible to define a unique standard
behavioral model that fits all OSN users’ behaviors. However, we expect that
similar people tend to follow the similar rules with the results of similar
behavioral models. For this reason, we propose a risk assessment organized into
two phases: similar users are first grouped together, then, for each identified
group, we build one or more models for normal behavior. The carried out
experiments on a real Facebook dataset show that the proposed model outperforms
a simplified behavioral-based risk assessment where behavioral models are built
over the whole OSN population, without a group identification phase.
CONCLUSION
In this paper, we proposed a two-phase risk assessment
approach able to assign a risk score to each OSN user. This risk estimation is
based on user’s behavior under the idea that the more this diverges from what
it can be considered as a ‘normal behavior’, the more the user should be
considered risky. Experiments carried out on a real Facebook dataset show the
effectiveness of our proposal. We plan to extend this work according to several
directions. An interesting future work is the extension of the proposed
two-phase risk assessment so as to make it able to perform a continuous monitoring
and estimation of risk scores. Moreover, we plan to revise the risk assessment
model so as to being deployable in Decentralized Online Social Networks, which
are characterized by the absence of a central source Any
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of data to be analyzed.
This will require to investigate decentralized data mining algorithms to gather
user features.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
• System : Pentium IV
2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44
Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
• Operating system : -
Windows XP/7.
• Coding Language :
JAVA/J2EE
• Data Base : MYSQL
REFERENCES
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of’active’and’silent’students. In Proceedings of the 9th international
conference on Computer supported collaborative learning-Volume 1, pages
132–136. International Society of the Learning Sciences, 2009. Any
Query Call Us: 9566355386
[3] Leyla Bilge, Thorsten Strufe, Davide Balzarotti,
and Engin Kirda. All your contacts are belong to us automated identity theft
attacks on social networks. In Proceedings of the 18th international conference
on World wide web, pages 551–560. ACM, 2009.