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A survey on application of machine learning for Internet of Things
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Lecture Notes in Computer Science 1205
(1999) · Industrial Robot: An International Journal · doi:10.1108/ir.1999.04926fae.001 · Cited by 38,662
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 2×
“…C4.5 decision tree, LinearSVC, random forest IoT devices 50–90 2017 [74] Human stress detection SVM, logistic regression Pulse waveform 68 2011 [75] Human activity recognition Logistic regression, multilayer perceptron Accelerometer data 90 2016 [76] Grape disease prediction Hidden markov…”
BackgroundMethodImproving network management with software defined networking
Kim, H., & Feamster, N. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6461195 · Cited by 1,259
Diagnosing network-wide traffic anomalies
Lakhina, A., Crovella, M., & Diot, C. (2004) · Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications · doi:10.1145/1015467.1015492 · Cited by 1,039
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT
Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A.-R., & Tarkoma, S. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.283 · Cited by 666
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K.-L. A., Elkhatib, Y., Hussain, A., & Al-Fuqaha, A. (2019) · IEEE Access · doi:10.1109/access.2019.2916648 · Cited by 575
Sketch-based change detection: methods, evaluation, and applications
Krishnamurthy, B., Sen, S., Zhang, Y., & Chen, Y. (2003) · Proceedings of the 2003 ACM SIGCOMM conference on Internet measurement - IMC '03 · doi:10.1145/948205.948236 · Cited by 528
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks
Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2727878 · Cited by 472
Machine Learning for Networking: Workflow, Advances and Opportunities
Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2018) · IEEE Network · doi:10.1109/mnet.2017.1700200 · Cited by 448
Research works printed but not linked: 14
- Juniper Research. Internet of things connected devices to almost triple to over 38 billion units by 2020. http://www.juniperresearch.com/press/press-releases/iot-connected-devices-to-triple-to-38-bn-by-2020
- Patel HJ, Temple MA, Baldwin RO (2015) Improving zigbee device network authentication using ensemble decision tree classifiers with radio frequency distinct native attribute fingerprinting. IEEE Trans Reliab 64(1):221–233 · doi:10.1109/tr.2014.2372432
- Huynh M, Nguyen P, Gruteser M, Vu T (2015) Poster: Mobile device identification by leveraging built-in capacitive signature. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. ACM, Denver, Colorado, pp 1635–1637 · doi:10.1145/2810103.2810118
- Baldini G, Dimc F, Kamnik R, Steri G, Giuliani R, Gentile C (2017) Identification of mobile phones using the built-in magnetometers stimulated by motion patterns. Sensors 17(4):783 · doi:10.3390/s17040783
- Caedo J, Skjellum A (2016) Using machine learning to secure IoT systems. In: 2016 14th annual conference on privacy, security and trust (PST), Auckland, pp 219–222
- Do VT, Engelstad P, Feng B, van Do T (2016) Strengthening mobile network security using machine learning. In: Younas M, Awan I, Kryvinska N, Strauss C, Thanh DV (eds) Mobile web and intelligent information systems. Springer International Publishing, Cham, pp 173–183 · doi:10.1007/978-3-319-44215-0_14
- Rathore H, Jha S (2013) Bio-inspired machine learning based wireless sensor network security. In: 2013 world congress on nature and biologically inspired computing. IEEE, Fargo, ND, pp 140–146 · doi:10.1109/nabic.2013.6617852
- Davis A, Parikh J, Weihl WE (2004) Edge computing: extending enterprise applications to the edge of the internet. In: International conference on World Wide Web—Alternate track papers & posters, WWW 2004. ACM, New York, NY, pp 180–187
- Drolia U, Guo K, Narasimhan P (2017) Precog: prefetching for image recognition applications at the edge. In: ACM/IEEE symposium on edge computing. ACM, San Jose, California, pp 1–13
- Hogan M, Esposito F (2017) Stochastic delay forecasts for edge traffic engineering via bayesian networks. In: IEEE international symposium on network computing and applications. IEEE, Cambridge, MA, pp 1–4 · doi:10.1109/nca.2017.8171341
- Taneja M (2016) A framework for traffic management in iot networks. In: 2016 2nd international conference on contemporary computing and informatics (IC3I). IEEE, Noida, pp 316–323 · doi:10.1109/ic3i.2016.7917982
- Ahmed ME, Kim H, Park M (2017) Mitigating dns query-based ddos attacks with machine learning on software-defined networking. In: MILCOM 2017—2017 IEEE military communications conference (MILCOM). IEEE, Baltimore, MD, pp 11–16 · doi:10.1109/milcom.2017.8170802
- Bhunia SS, Gurusamy M (2017) Dynamic attack detection and mitigation in iot using sdn. In: 2017 27th international telecommunication networks and applications conference (ITNAC). IEEE, Melbourne, VIC, pp 1–6
- Madeira R, Nunes L (2016) A machine learning approach for indirect human presence detection using IoT devices. In: 2016 eleventh international conference on digital information management (ICDIM). IEEE, Porto, pp 145–150 · doi:10.1109/icdim.2016.7829781
Cited by
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
Network Intrusion Detection for IoT Security Based on Learning Techniques
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2896380 · Cited by 940
A Survey on Federated Learning for Resource-Constrained IoT Devices
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774
Reliable Federated Learning for Mobile Networks
Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900119 · Cited by 617
Machine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 470
Metadata
- Authors
- Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J.
- Venue
- International Journal of Machine Learning and Cybernetics
- Published
- 2018-06-11
- Volume
- 9
- Issue
- 8
- Pages
- 1399-1417
- ISSN
- 1868-8071 (ISSN-L)
- 1868-808X
- OpenAlex
- W2808420356
- Cited by
- 293
- References
- 65
- FWCI
- 38.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Funders
- National Natural Science Foundation of China (61772345)
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- Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J. (2018). A survey on application of machine learning for Internet of Things. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-018-0834-5
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
