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.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Naeimeh Laleh,
Barbara Carminati and Elena Ferrari, “Risk Assessment
in Social Networks based on User Anomalous Behaviours”, IEEE Transactions on
Dependable and Secure Computing, 2018.