Description
Title: CLASSIFICATION OF TRAFFIC USING DEEP-LEARNING RECURRENT LSTM OVER COLLABORATIVE IoT/CLOUD PLATFORMS
Abstract: Over the past few decades, new infrastructures like the Internet of Things (IoT) and cloud-based collaborative platforms have emerged. IoT/cloud-based collaborative platforms must classify network traffic into benign and malicious types in order to use channel capacity efficiently for transmitting benign traffic and blocking malicious traffic. In order to identify malicious traffic earlier and quickly direct benign traffic to the intended nodes, the traffic classification mechanism needs to be dynamic and quick enough to classify network traffic. In this paper, we present a Word2Vec-based deep-learning recurrent LSTM RNet technique for categorizing traffic over IoT/cloud platforms. Additionally, machine learning techniques (MLTs) have been used to assess how well they perform in comparison to the LSTM RNet classification method that has been put forth. Network traffic in the proposed research project is divided into three categories: Tor-Normal, Non-Tor-Normal, and Non-Tor-Malicious traffic. The study’s findings demonstrate that the suggested LSTM RNet classifies such traffic accurately, reduces network latency, and improves data transmission rates and network throughput.
Keywords: IoT, network traffic, machine learning, deep learning, classification, cloud computing
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
Plagiarism Report: Turnitin Plagiarism Report will be less than 10%
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