ABSTRACT:
An industrial mobile network is
crucial for industrial production in the Internet of Things. It guarantees the
normal function of machines and the normalization of industrial production.
However, this characteristic can be utilized by spammers to attack others and
influence industrial production. Users who only share spams,
such as links to viruses and advertisements, are called spammers. With the
growth of mobile network membership, spammers have organized into groups for
the purpose of benefit maximization, which has caused confusion and heavy
losses to industrial production. It is difficult to distinguish spammers from
normal users owing to the characteristics of multidimensional data. To address
this problem, this paper proposes a Spammer Identification scheme based on
Gaussian Mixture Model (SIGMM) that utilizes machine learning for industrial
mobile networks. It provides intelligent identification of spammers without
relying on flexible and unreliable relationships. SIGMM combines the
presentation of data, where each user node is classified into one class in the
construction process of the model. We validate SIGMM by comparing it with the
reality mining algorithm and hybrid FCM clustering algorithm using a mobile
network dataset from a cloud server. Simulation results show that SIGMM
outperforms these previous schemes in terms of recall, precision, and time
complexity.
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:
Tie Qiu, Senior Member, IEEE, Heyuan
Wang, Keqiu Li, Senior Member, IEEE, Huansheng Ning, Senior Member,
IEEE, Arun Kumar Sangaiah, Baochao Chen, “A Novel Machine Learning Algorithm for
Spammer Identification in Industrial Mobile Cloud Computing”, IEEE Transactions
on Industrial Informatics, 2018.