| Abstrakt: |
The majority of people are using connecting media in recent times, like Facebook, Twitter, LinkedIn, etc. People like to communicate on social websites as compared to direct conversation due to the lack of time, fast communication, minimizing long distance, etc. but more fraud such as fake accounts, cloned attacks, etc. are generated due to growing data on social connecting media. In our proposed approach, we are using the Bernoulli RBM_RF Classifier (BRRC) to detect cloned attacks in connecting media. This model is based on deep learning algorithms and more research is under process but still, there is no such kind of model to provide high accuracy. In this model, we have used multiple algorithms Artificial Neural Networks (ANN), Logistic Regression, Decision Trees, K-Nearest Neighbour Classification, Random Forest, and Bernoulli RBM with extra add-on features of the random forest we have created a Bernoulli pipeline also known as BRRC. Our goal is to establish a clear neural network connection between our target attributes. Finally, we created a restricted Boltzmann machine using a RBM Bernoulli pipeline with additional add-on random forest features after using random forest, where we employed n number of decision trees for classification and provided the same in depth. Our model can extract higher-level features from the given data and helps in the dimensionality Reduction of the input data by learning a compressed representation. Accuracy can be improved by using a big dataset with particular connecting media attributes that are either not currently available or are only sparingly present. We can conclude that Bernoulli Restricted Boltzmann Machine is the best fit and suitable for our model with 93% accuracy. [ABSTRACT FROM AUTHOR] |
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