The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Internet of Things: A survey on machine learning-based intrusion detection approaches
Open accessAbstract
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.
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Cites
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes
Ali, B., & Awad, A. (2018) · Sensors · doi:10.3390/s18030817 · Cited by 344
Cited by
Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4150 · Cited by 1,288
Khraisat, A., & Alazab, A. (2021) · Cybersecurity · doi:10.1186/s42400-021-00077-7 · Cited by 508
A machine learning-based intrusion detection for detecting internet of things network attacks
Kayode Saheed, Y., Idris Abiodun, A., Misra, S., Kristiansen Holone, M., & Colomo-Palacios, R. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.02.063 · Cited by 371
Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., & Xiong, N. N. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3089435 · Cited by 360
Application of deep reinforcement learning to intrusion detection for supervised problems
Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112963 · Cited by 360
Metadata
- Authors
- da Costa, K. A. P., Papa, J. P., Lisboa, C. O., Munoz, R., & de Albuquerque, V. H. C.
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 151
- Pages
- 147-157
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W2913497771
- Cited by
- 520
- References
- 2
- FWCI
- 36.90
- Type
- journal article
- Language
- en
- Open access
- green
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- da Costa, K. A. P., Papa, J. P., Lisboa, C. O., Munoz, R., & de Albuquerque, V. H. C. (2019). Internet of Things: A survey on machine learning-based intrusion detection approaches. Computer Networks. https://doi.org/10.1016/j.comnet.2019.01.023
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