Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach
Abstract
The popularity of Internet of Things (IoT) devices in smart homes has raised significant concerns regarding data security and privacy. Traditional machine learning (ML) methods for anomaly detection often require sharing sensitive IoT data with a central server, posing security and efficiency challenges. In response, this paper introduces FedGroup, a novel Federated Learning (FL) method inspired by FedAvg. FedGroup revolutionizes the central model’s learning process by updating it based on the learning patterns of distinct groups of IoT devices. Our experimental results demonstrate that FedGroup consistently achieves comparable or superior accuracy in anomaly detection when compared to both federated and non-federated learning methods. Additionally, Ensemble Learning (EL) collects intelligence from numerous contributing models, leading to enhanced prediction performance. Furthermore, FedGroup significantly improves the detection of attack types and their details, contributing to a more robust security framework for smart homes. Our approach demonstrates exceptional performance, achieving an accuracy rate of 99.64% with a minimal false positive rate (FPR) of 0.02% in attack type detection, and an impressive 99.89% accuracy in attack type detail detection.
References
[1]
Abu Al-Haija Q, Al Badawi A, and Bojja GR Boost-defence for resilient iot networks: a head-to-toe approach Expert Systems 2022 39 10
[2]
Abu Al-Haija, Q., Al-Dala’ien, M.: Elba-iot: An ensemble learning model for botnet attack detection in iot networks. Journal of Sensor and Actuator Networks 11(1) (2022)., https://www.mdpi.com/2224-2708/11/1/18
[3]
Al-Haija, Q.A., McCurry, C.D., Zein-Sabatto, S.: Intelligent self-reliant cyber-attacks detection and classification system for IoT communication using deep convolutional neural network. In: Selected Papers from the 12th International Networking Conference, pp. 100–116. Springer International Publishing (2021).,
[4]
Ali, M.H.: Smart home security: Security and vulnerabilities. Wevolver (2021), https://www.wevolver.com/article/smart-home-security-security-and-vulnerabilities
[5]
Alsaedi A, Moustafa N, Tari Z, Mahmood A, and Anwar A Ton\_iot telemetry dataset: a new generation dataset of iot and iiot for data-driven intrusion detection systems IEEE Access 2020 8 165130-165150
[6]
Andrade, L., Prost-Boucle, A., Pétrot, F.: Overview of the state of the art in embedded machine learning. In: 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). pp. 1033–1038. IEEE (2018)
[7]
Bonawitz, K.e.a.: Towards federated learning at scale: System design. (2019), http://arxiv.org/abs/1902.01046
[8]
Breiman, L.: Bagging predictors. Machine Learning. 24 (1996).,
[9]
Brownlee, J.: Failure of classification accuracy for imbalanced class distributions. (2021), https://machinelearningmastery.com/failure-of-accuracy-for-imbalanced-class-distributions/
[10]
Brownlee, J.: A gentle introduction to ensemble learning algorithms. machine learning mastery. (2021), https://machinelearningmastery.com/tour-of-ensemble-learning-algorithms/
[11]
Burke, D., J. Brundage, R.R.: Measurement of the false positive rate in a screening program for human immunodeficiency virus infections. The New England Journal of Medicine (1988).
[12]
Campos, E.M., Saura, P.F., González-Vidal, A., Hernández-Ramos, J.L., Bernabé, J.B., Baldini, G., Skarmeta, A.: Evaluating federated learning for intrusion detection in internet of things: Review and challenges. Computer Networks 203, 108661 (2022). www.sciencedirect.com/science/article/pii/S1389128621005405
[13]
Colquhoun, D.: An investigation of the false discovery rate and the misinterpretation of p values. 1, 140216 (2014).
[14]
David R, Duke J, Jain A, Janapa Reddi V, Jeffries N, Li J, Kreeger N, Nappier I, Natraj M, Wang T, et al. Tensorflow lite micro: Embedded machine learning for tinyml systems Proceedings of Machine Learning and Systems 2021 3 800-811
[15]
Deloitte: What is digital economy? | unicorns, transformation and the internet of things: Deloitte malta. Deloitte (2021), https://www2.deloitte.com/mt/en/pages/technology/articles/mt-what-is-digital-economy.html
[16]
Gour, L., Waoo, A.A.: Fault-tolerant framework with federated learning for reliable and robust distributed system. Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India (2022).
[17]
Habibi Gharakheili H, Sivanathan A, Hamza A, and Sivaraman V Network-level security for the internet of things: Opportunities and challenges Computer 2019 52 8 58-62
[18]
Hamza, A., Gharakheili, H.H., Benson, T.A., Sivaraman, V.: Detecting volumetric attacks on lot devices via sdn-based monitoring of mud activity. In: Proceedings of the 2019 ACM Symposium on SDN Research. p. 36–48. SOSR ’19, Association for Computing Machinery, New York, NY, USA (2019).
[19]
J. Vanerio, P.C.: Ensemble-learning approaches for network security and anomaly detection. In: Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. pp. 1–6. Los Angeles CA USA (2017).
[20]
Lasquety-Reyes, J.: Number of smart homes forecast in the world from 2017 to 2025. Statista (June 2021), https://www.statista.com/forecasts/887613/number-of-smart-homes-in-the-smart-home-market-in-the-world
[21]
Li, L., Fan, Y., Tse, M., Lin, K.Y.: A review of applications in federated learning. Computers & Industrial Engineering 149, 106854 (2020). www.sciencedirect.com/science/article/pii/S0360835220305532
[22]
Li, T., Sanjabi, M., Beirami, A., Smith, V.: Fair resource allocation in federated learning. ICLR (2020), http://arxiv.org/abs/1905.10497
[23]
M. Mohri, G. Sivek, A.T.S.: Agnostic federated learning. arXiv p. 11 (2019)
[24]
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: International Conference on Artificial Intelligence and Statistics (2016), https://api.semanticscholar.org/CorpusID:14955348
[25]
McMahan, H.B., Ramage, D.: Federated learning: Collaborative machine learning without centralized training data. Google (2017), https://research.googleblog.com/2017/04/federated-learning-collaborative.html
[26]
Morgan, S.: Cybercrime to cost the world $10.5 trillion annually by 2025. Cybercrime Magazine (2020), https://cybersecurityventures.com/hackerpocalypse-cybercrime- report-2016/
[27]
n.d.: Credit card fraud detection: Everything you need to know. Inscribe (2023), https://www.inscribe.ai/fraud-detection/credit-fraud-detection
[28]
Risteska Stojkoska, B.L., Trivodaliev, K.V.: A review of internet of things for smart home: Challenges and solutions. Journal of Cleaner Production 140, 1454–1464 (2017). www.sciencedirect.com/science/article/pii/S095965261631589X
[29]
Sandro, N.: Internet of things (iot): Opportunities, issues and challenges towards a smart and sustainable future. Journal of Cleaner Production (2020). linkinghub.elsevier.com/retrieve/pii/S095965262032922X
[30]
Shuhaiber, A., Mashal, B.: Understanding users’ acceptance of smart homes. Technology in Society (2019). linkinghub.elsevier.com/retrieve/pii/S0160791X18300484
[31]
Sivanathan A, Gharakheili HH, Loi F, Radford A, Wijenayake C, Vishwanath A, and Sivaraman V Classifying iot devices in smart environments using network traffic characteristics IEEE Transactions on Mobile Computing 2019 18 8 1745-1759
[32]
Sivaraman V, Gharakheili HH, Fernandes C, Clark N, and Karliychuk T Smart iot devices in the home: Security and privacy implications IEEE Technology and Society Magazine 2018 37 2 71-79
[33]
Thudumu S, Branch P, Jin J, and Singh J A comprehensive survey of anomaly detection techniques for high dimensional big data Journal of Big Data 2020
[34]
Truong, N., Sun, K., Wang, S., Guitton, F., Guo, Y.: Privacy preservation in federated learning: An insightful survey from the gdpr perspective. Computers & Security 110, 102402 (2021). www.sciencedirect.com/science/article/pii/S0167404821002261
[35]
Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: A review. Expert Systems with Applications 36(10), 11994–12000 (2009). www.sciencedirect.com/science/article/pii/S0957417409004801
[36]
Wei, K., Li, J., Ding, M., Ma, C., Yang, H.H., Farokhi, F., Jin, S., Quek, T.Q.S., Vincent Poor, H.: Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security 15, 3454–3469 (2020).
[37]
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. 10(2) (jan 2019).
[38]
Yu, P., Wynter, L., Lim, S.H.: Fed+: A family of fusion algorithms for federated learning. CoRR abs/2009.06303 (2020), https://arxiv.org/abs/2009.06303
[39]
Zhang Y, Suleiman B, and Alibasa MJ Longfei S and Bodhi P Fedgroup: a federated learning approach for anomaly detection in iot environments Mobile and Ubiquitous Systems: Computing, Networking and Services 2023 Cham Springer Nature Switzerland 121-132
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© The Author(s) 2023.
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Plenum Press
United States
Publication History
Published: 04 January 2024
Accepted: 13 October 2023
Revision received: 08 October 2023
Received: 14 June 2023
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