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    Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: a survey

    Shafiq, Muhammad, Tian, Zhihong, Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522, Jolfaei, Alireza and Yu, Xiangzhan (2020) Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: a survey. Sustainable Cities and Society, 60. p. 102177. ISSN 2210-6707

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    Abstract

    This survey paper describes the significant literature survey of Sustainable Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification. Considering relevance and most cited methods and datasets of features were identified, read and summarized. As data and data features are essential in Internet traffic classification using machine learning techniques, some well-known and most used datasets with details statistical features are described. Different classification techniques for SSC network traffic classification are presented with more information. The complexity of data set, features extraction and machine learning methods are addressed. In the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.

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