e-space
Manchester Metropolitan University's Research Repository

Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey

Shafiq, Muhammad and Tian, Zhihong and Bashir, Ali Kashif and Jolfaei, Alireza and Yu, Xiangzhan (2020) Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey. Sustainable Cities and Society. p. 102177. ISSN 2210-6707

[img]
Restricted to Repository staff only until 17 May 2022.

Download (1MB)

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.

Impact and Reach

Statistics

Downloads
Activity Overview
0Downloads
29Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

Edit Item Edit Item