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International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

SECURING LARGE-SCALE IOT NETWORKS: A FEDERATED TRANSFER LEARNING APPROACH FOR REAL-TIME INTRUSION DETECTION

DOI https://doi.org/10.55640/ijmcsit-v02i06-01
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Dr. Sofia Duarte 1 , Jiwon Park 1 ,
4 Department of Computer Engineering, University of Lisbon, Portugal
4 Department of Computer Engineering, University of Lisbon, Portugal

Abstract

The pervasive deployment of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and data generation. However, this expansive network also presents a vast attack surface, making robust intrusion detection critical. Traditional centralized Intrusion Detection Systems (IDS) face significant challenges in large-scale IoT environments, including privacy concerns, communication overhead, and the sheer volume and heterogeneity of data. This article proposes an enhanced real-time intrusion detection framework that leverages the synergistic capabilities of Federated Learning (FL) and Transfer Learning (TL). The framework allows IoT devices to collaboratively train a global intrusion detection model without sharing raw data, thereby preserving privacy, while utilizing pre-trained knowledge to enhance detection capabilities and adapt to evolving threats. We discuss the architectural components, data handling strategies, and the integration of FL and TL, highlighting how this approach can significantly improve detection accuracy, reduce latency, and maintain data privacy in dynamic and resource-constrained large-scale IoT networks.

Keywords

IoT security, large-scale networks, federated learning, transfer learning

References

Z. Almesleh, A. Gouissem and R. Hamila, "Federated Learning with Kalman Filter for Intrusion Detection in IoT Environment," 2024 IEEE 8th Energy Conference (ENERGYCON), Doha, Qatar, 2024, pp. 1-6, doi: 10.1109/ENERGYCON58629.2024.10488796.
Salunkhe UR, Mali SN. Security enrichment in intrusion detection system using classifier ensemble. Journal of Electrical and Computer Engineering. 2017;2017(1):1794849.
Vengatesan K, Kumar A, Naik R, Verma DK. Anomaly based novel intrusion detection system for network traffic reduction. In2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on 2018 Aug 30 (pp. 688-690). IEEE.
T. Ohtani, R. Yamamoto and S. Ohzahata, "Detecting Zero-Day Attack with Federated Learning Using Autonomously Extracted Anomalies in IoT," 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2024, pp. 356-359, doi: 10.1109/CCNC51664.2024.10454669.
Khraisat A, Gondal I, Vamplew P, Kamruzzaman J. Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity. 2019 Dec;2(1):1-22.
Mohammad RM, Alsmadi MK, Almarashdeh I, Alzaqebah M. An improved rule induction based denial of service attacks classification model. Computers & Security. 2020 Dec 1;99:102008.
Muneer S, Farooq U, Athar A, Ahsan Raza M, Ghazal TM, Sakib S. A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis. Journal of Engineering. 2024;2024(1):3909173.
Saied M, Guirguis S, Madbouly M. Review of artificial intelligence for enhancing intrusion detection in the internet of things. Engineering Applications of Artificial Intelligence. 2024 Jan 1;127:107231.
Alsmadi MK, Mohammad RM, Alzaqebah M, Jawarneh S, AlShaikh M, Al Smadi A, Alghamdi FA, Alqurni JS, Alfagham H. Intrusion Detection Using an Improved Cuckoo Search Optimization Algorithm.
Latif S, Boulila W, Koubaa A, Zou Z, Ahmad J. Dtl-ids: An optimized intrusion detection framework using deep transfer learning and genetic algorithm. Journal of Network and Computer Applications. 2024 Jan 1;221:103784.
Zhu J, Liu X. An integrated intrusion detection framework based on subspace clustering and ensemble learning. Computers and Electrical Engineering. 2024 Apr 1;115:109113.
Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y. A survey on federated learning. Knowledge-Based Systems. 2021 Mar 15;216:106775.
Li L, Fan Y, Tse M, Lin KY. A review of applications in federated learning. Computers & Industrial Engineering. 2020 Nov 1;149:106854.
Khan LU, Saad W, Han Z, Hossain E, Hong CS. Federated learning for internet of things: Recent advances, taxonomy, and open challenges. IEEE Communications Surveys & Tutorials. 2021 Jun 18;23(3):1759-99.
M. Umair, W. -H. Tan and Y. -L. Foo, "Dynamic Federated Learning Aggregation for Enhanced Intrusion Detection in IoT Attacks," 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Osaka, Japan, 2024, pp. 524-529, doi: 10.1109/ICAIIC60209.2024.10463247.
Iman M, Arabnia HR, Rasheed K. A review of deep transfer learning and recent advancements. Technologies. 2023 Mar 14;11(2):40.
Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. Journal of Big data. 2016 Dec;3:1-40.
A. U. Karimy and P. C. Reddy, "Analyzing Federated Learning as a novel approach for enhancing security and privacy in the Internet of Things (IoT)," 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2024, pp. 1-7, doi: 10.1109/ICAECT60202.2024.10468686.
Y. Luan, "Network Traffic Anomaly Detection Based on Federated Learning," 2024 4th International Conference on Neural Networks, Information and Communication Engineering (NNICE), Guangzhou, China, 2024, pp. 224-228, doi: 10.1109/NNICE61279.2024.10498908.
M. H. Bhavsar, Y. B. Bekele, K. Roy, J. C. Kelly and D. Limbrick, "FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT," in IEEE Access, vol. 12, pp. 52215-52226, 2024, doi: 10.1109/ACCESS.2024.3386631.
H. Babbar and S. Rani, "FRHIDS: Federated Learning Recommender Hybrid Intrusion Detection System Model in Software-Defined Networking for Consumer Devices," in IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 2492-2499, Feb. 2024, doi: 10.1109/TCE.2023.3329151.
A. Raj, V. Sharma, S. Rani, A. K. Shanu and N. Kumar, "Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study," 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2024, pp. 1-5, doi: 10.1109/ICRITO61523.2024.10522388.
M. Al-Hawawreh and M. S. Hossain, "Federated Learning-Assisted Distributed Intrusion Detection Using Mesh Satellite Nets for Autonomous Vehicle Protection," in IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 854-862, Feb. 2024, doi: 10.1109/TCE.2023.3318723.
Andresini G, Appice A, De Rose L, Malerba D. GAN augmentation to deal with imbalance in imaging-based intrusion detection. Future Generation Computer Systems. 2021 Oct 1;123:108-27.
Elreedy, D.; Atiya, A.F. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf. Sci. 2019, 505, 32–64.
Mohammad R, Saeed F, Almazroi AA, Alsubaei FS, Almazroi AA. Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach. Systems. 2024 Mar 1;12(3):79.
BoT-IoT Dataset. (2018). Retrieved from https://research.unsw.edu.au/projects/bot-iot-dataset
Kashif, M. (2019). N-BaIoT Dataset. Kaggle. Retrieved from https://www.kaggle.com/datasets/mkashifn/nbaiot-dataset/code
TON_IoT Dataset. (2020). Retrieved from https://research.unsw.edu.au/projects/ton-iot-datasets
Huhn, C. (2017). CICIDS 2017. Kaggle. Retrieved from https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset
Hassan, M. (2019). NSL-KDD Dataset. Kaggle. Retrieved from https://www.kaggle.com/datasets/hassan06/nslkdd

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