Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM

Abstract

Internet of Things (IoT) and cloud based collaborative platforms are emerging as new infrastructures during recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT-cloud based collaborative platforms to utilize the channel capacity optimally for transmitting the benign traffic and to block the malicious traffic. The traffic classification mechanism should be dynamic and capable enough to classify the network traffic in a quick manner, so that the malevolent traffic can be identified in earlier stages and benign traffic can be channelized to the destined nodes speedily. In this paper, we are presenting deep learning recurrent LSTM based technique to classify the traffic over IoT-cloud platforms. Machine learning techniques (MLTs) have also been employed for comparison of the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is classified into three classes namely Tor-Normal, NonTor-Normal and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet classify the traffic accurately and also helps in reducing the network latency and in enhancing the data transmission rate as well as network throughput.

LUNAR: A Practical Anonymous Network Simulation Platform

“…Reference [19] develops, investigates, and evaluates the performance of machine-learning-based darknet traffic detection systems (DTDS) in IoT networks. Reference [20] presents deep learning recurrent LSTM-based technique to classify the traffic over IoT-cloud platforms. Reference [21] realizes resource-conserving access control and end-to-end security for IoT devices and deploys onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage resources to achieve the same grade of anonymity as readily available to traditional devices.…”