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

By Kelton Augusto Pontara Da Costa, João Paulo Papa, Celso O. Lisboa, Roberto Muñoz, V. H. C. de Albuquerque. Comput. Networks, 2019

455 citations recorded by the source corpus.

Abstract 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 IoT environments.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8ec4b08a83faf5622fea30888a7fe14b7b0baf9b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “…role in numerous applications of the cyber security for early detection and prediction of different attacks such as spam classiﬁcation [29]–[32], fraud detection [33]–[36], malware detection [37]–[40], phishing [41]–[43], darkweb or deep-web sites [44], [45], and intrusion detection [46]–[49].”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, methodology): “2019 [10] ML-based techniques for IDS in IoT Sec.”