Enhancing IoT anomaly detection performance for federated learning

Weinger, Brett; Kim, Jinoh; Sim, Alex; Nakashima, Makiya; Moustafa, Nour; Wu, K. John

doi:10.1016/j.dcan.2022.02.007

Title: Enhancing IoT anomaly detection performance for federated learning

Journal Article · 31 May 2022 · Digital Communications and Networks

DOI: https://doi.org/10.1016/j.dcan.2022.02.007 · OSTI ID:1859795

; Kim, Jinoh; Sim, Alex; Nakashima, Makiya; Moustafa, Nour; Wu, K. John

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1859795

Journal Information:: Digital Communications and Networks, Journal Name: Digital Communications and Networks Journal Issue: 3 Vol. 8; ISSN 2352-8648

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (16)

A study of the stratified random sampling Aoyama, Hirojiro Annals of the Institute of Statistical Mathematics, Vol. 6, Issue 1 https://doi.org/10.1007/BF02960514	journal	December 1954
Capabilities and limitations of wireless CO2, temperature and relative humidity sensors Mylonas, Angelos; Kazanci, Ongun Berk; Andersen, Rune K. Building and Environment, Vol. 154 https://doi.org/10.1016/j.buildenv.2019.03.012	journal	May 2019
Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings Plageras, Andreas P.; Psannis, Kostas E.; Stergiou, Christos Future Generation Computer Systems, Vol. 82 https://doi.org/10.1016/j.future.2017.09.082	journal	May 2018
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches Hasan, Mahmudul; Islam, Md. Milon; Zarif, Md Ishrak Islam Internet of Things, Vol. 7 https://doi.org/10.1016/j.iot.2019.100059	journal	September 2019
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks Sandfort, Veit; Yan, Ke; Pickhardt, Perry J. Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-52737-x	journal	November 2019
Secure Medical Data Transmission Model for IoT-Based Healthcare Systems Elhoseny, Mohamed; Ramirez-Gonzalez, Gustavo; Abu-Elnasr, Osama M. IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2018.2817615	journal	January 2018
Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering Lyu, Lingjuan; Jin, Jiong; Rajasegarar, Sutharshan IEEE Internet of Things Journal, Vol. 4, Issue 5 https://doi.org/10.1109/JIOT.2017.2709942	journal	October 2017
PrivacyProtector: Privacy-Protected Patient Data Collection in IoT-Based Healthcare Systems Luo, Entao; Bhuiyan, Md Zakirul Alam; Wang, Guojun IEEE Communications Magazine, Vol. 56, Issue 2 https://doi.org/10.1109/MCOM.2018.1700364	journal	February 2018
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders Meidan, Yair; Bohadana, Michael; Mathov, Yael IEEE Pervasive Computing, Vol. 17, Issue 3 https://doi.org/10.1109/MPRV.2018.03367731	journal	July 2018
Federated Learning: Challenges, Methods, and Future Directions Li, Tian; Sahu, Anit Kumar; Talwalkar, Ameet IEEE Signal Processing Magazine, Vol. 37, Issue 3 https://doi.org/10.1109/MSP.2020.2975749	journal	May 2020
Anomaly detection: A survey Chandola, Varun; Banerjee, Arindam; Kumar, Vipin ACM Computing Surveys, Vol. 41, Issue 3, p. 1-58 https://doi.org/10.1145/1541880.1541882	journal	July 2009
User Perceptions of Smart Home IoT Privacy Zheng, Serena; Apthorpe, Noah; Chetty, Marshini Proceedings of the ACM on Human-Computer Interaction, Vol. 2, Issue CSCW https://doi.org/10.1145/3274469	journal	November 2018
Generative adversarial networks Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi Communications of the ACM, Vol. 63, Issue 11 https://doi.org/10.1145/3422622	journal	October 2020
Deep Learning for Anomaly Detection: A Review Pang, Guansong; Shen, Chunhua; Cao, Longbing ACM Computing Surveys, Vol. 54, Issue 2 https://doi.org/10.1145/3439950	journal	March 2022
Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study Preuveneers, Davy; Rimmer, Vera; Tsingenopoulos, Ilias Applied Sciences, Vol. 8, Issue 12 https://doi.org/10.3390/app8122663	journal	December 2018
IoT Privacy and Security Challenges for Smart Home Environments Lin, Huichen; Bergmann, Neil Information, Vol. 7, Issue 3 https://doi.org/10.3390/info7030044	journal	July 2016

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97 MATHEMATICS AND COMPUTING
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