Deep Learning CNN for Detecting Malicious Social Bots

Abstract

The Public are considerably using the various types of online social networks (OSNs) and it is
become more common in people’s social life. Thus, the users are facing spam relate issues and
fake accounts due to Out-of-controlOSNs evolution, due to these attacks users personal
information is remains unsafe. To solve these problems, various types of machine learning
algorithms are proposed by the various Researchers.But these methods are failed to detect the
bots, spam detection and fake accounts detection effectively with maximum accuracy. Thus, this
paper proposes to use the Deep Learning Convolutional Neural Network (DLCNN)as a modern
algorithm to effectively identify suspected ClickstreamSequences and bots, to add choices and to
restrict measurements. The classification mastering algorithmis used to determine the actual or
false identity of target fake accounts. From the extensive simulation results, it is observed that
the proposed DLCNN consumes less training time and provides highest classification accuracy
compared to the state of art approaches.