Classification of traffic over collaborative iot/cloud platforms using deep-learning recurrent LSTM

• Autorzy: Patil Sonali A. , Raj Arun L.

Identyfikatory

DOI

10.7494/csci.2021.22.3.3968

• Języki publikacji: EN

Abstrakty

EN

The Internet of Things (IoT) and cloud-based collaborative platforms have emerged as new infrastructures over the recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT/cloud-based collaborative platforms for optimally utilizing channel capac ity for transmitting benign traffic and blocking malicious traffic. The traffic classification mechanism should be dynamic and capable enough for classifying network traffic in a quick manner so that malevolent traffic can be identified at earlier stages and benign traffic can be speedily channelized to the destined nodes. In this paper, we present a deep-learning recurrent LSTM RNet-based technique for classifying traffic over IoT/cloud platforms using the Word2Vec approach. Machine-learning techniques (MLTs) have also been employed for comparing the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is clas sified into three classes: Tor-Normal, NonTor-Normal, and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet accurately classifies such traffic and also helps reduce network latency as well as enhance data transmission rates and network throughput.

Słowa kluczowe

EN

IoT; network traffic; machine learning; classification; cloud computing

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2021
• Tom: T. 22 (3)
• Strony: 367–385
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Patil Sonali A.

• Department of Computer Sc. & Engg, B.S. Abdur Crescent Institute of Science & Technology, Tamil Nadu, India

autor

Raj Arun L.

• Department of Computer Sc. & Engg, B.S. Abdur Crescent Institute of Science & Technology, Tamil Nadu, India

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Uwagi

PL

„Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).”