期刊
ELECTRONICS
卷 11, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/electronics11091502
关键词
cyber attacks; cyber-physical systems; deep learning; denial-of-service (DoS); detection methods; Internet of Things (IoT); machine learning; man-in-the-middle; security
This article discusses the IoT system and its security issues, focusing on the application of learning-based methods in detecting network attacks in IoT systems. By integrating existing literature, a comprehensive overview of the developments in this field is provided, along with future research directions.
Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remoteto-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.
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