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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Internet of Things: A survey on machine learning-based intrusion detection approaches

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da Costa, Kelton A. P. [1] ; Papa, Joao P. [1] ; Lisboa, Celso O. [1] ; Munoz, Roberto [2] ; de Albuquerque, Victor Hugo C. [3]
Total Authors: 5
[1] Sao Paulo State Univ, Dept Comp, Bauru - Brazil
[2] Univ Valparaiso, Sch Informat Engn, Valparaiso - Chile
[3] Univ Fortaleza, Grad Program Appl Informat, Fortaleza, Ceara - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Computer Networks; v. 151, p. 147-157, MAR 14 2019.
Web of Science Citations: 3

In the world scenario, concerns with security and privacy regarding computer networks are always increasing. Computer security has become a necessity due to the proliferation of information technologies in everyday life. The increase in the number of Internet accesses and the emergence of new technologies, such as the Internet of Things (IoT paradigm, are accompanied by new and modern attempts to invade computer systems and networks. Companies are increasingly investing in studies to optimize the detection of these attacks. Institutions are selecting intelligent techniques to test and verify by comparing the best rates of accuracy. This research, therefore, focuses on rigorous state-of-the-art literature on Machine Learning Techniques applied in Internet-of-Things and Intrusion Detection for computer network security. The work aims, therefore, recent and in-depth research of relevant works that deal with several intelligent techniques and their applied intrusion detection architectures in computer networks with emphasis on the Internet of Things and machine learning. More than 95 works on the subject were surveyed, spanning across different themes related to security issues in loT environments. (C) 2019 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/22905-6 - About image security using machine learning
Grantee:Kelton Augusto Pontara da Costa
Support type: Regular Research Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support type: Regular Research Grants