TY - JOUR AU - Patil, Sonali AU - Raj, L. Arun PY - 2021/09/30 Y2 - 2024/03/28 TI - Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM JF - Computer Science JA - csci VL - 22 IS - 3 SE - Articles DO - 10.7494/csci.2021.22.3.3968 UR - https://journals.agh.edu.pl/csci/article/view/3968 SP - AB - 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. ER -