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An Ensemble Deep Neural Network Model for Onion-Routed Traffic Detection to Boost Cloud Security
Abstract
Anonymous network communication using onion routing networks such as Tor are used to guard the privacy of sender by encrypting all messages in the overlapped network. These days most of the onion routed communications are not only used for decent cause but also cyber offenders are ill-using onion routings for scanning the ports, hacking, exfiltration of theft data, and other types of online frauds. These cyber-crime attempts are very vulnerable for cloud security. Deep learning is highly effective machine learning method for prediction and classification. Ensembling multiple models is an influential approach to increase the efficiency of learning models. In this work, an ensemble deep learning-based classification model is proposed to detect communication through Tor and non-Tor network. Three different deep learning models are combined to achieve the ensemble model. The proposed model is also compared with other machine learning models. Classification results shows the superiority of the proposed model than other models.
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