5G Internet of Things intrusion detection method and system based on federated transfer learning

Abstract

The invention provides a 5G Internet of Things intrusion detection method and system based on federated transfer learning. The 5G Internet of Things intrusion detection method comprises the steps of:collecting a to-be-detected traffic in the Internet of Things, and acquiring a feature vector of an original data packet; and inputting the feature vector into a corresponding client model fm, k, andjudging whether the to-be-detected traffic is legal or not. According to the 5G Internet of Things intrusion detection, the federation transfer learning method in the 5G Internet of Things IDS is proposed for the first time, data from different Internet of Things can be safely aggregated, a good intrusion detection model for each Internet of Things is realized through knowledge transfer and sharing, the 5G Internet of Things intrusion detection method can be conveniently and safely applied to various different Internet of Things, and the method has very strong generalization ability and is suitable for popularization and application. Compared with an existing method, abnormal traffic can be detected more accurately, and unknown attacks can be detected more effectively.

Description

technical field [0001] The invention belongs to the field of the Internet of Things, and in particular relates to a 5G Internet of Things intrusion detection method and system based on federated transfer learning. Background technique [0002] The field of IoT intrusion detection has been extensively studied. In IPv6-connected Internet of Things, [S.Raza, L.Wallgren, and T.Voigt, "SVELTE: Real-time intrusion detection in the Internet of Things," Ad hoc networks, vol.11, no.8, pp. 2661-2674, 2013] for the first time proposed a lightweight IDS to detect spoofing and vulnerability attacks. [H.Bostani, and M.Sheikhan, "Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach," Computer Communications, vol.98, pp.52-71, 2017] proposed a A hybrid-mode IoT intrusion detection system that supports the detection of trapping and selective forwarding attacks in 6LowPAN networks. Both approaches can be extended to d...

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Problems solved by technology

Therefore, such a solution hardly reflects the real world situation

[0009] [T.D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A.-R. Sadeghi," A federated self-learning anomaly detection system for IoT,"in 2019IEEE 39th International Conference on Distributed Computing Systems(ICDCS).IEEE,pp.756-76.] Federated learning is used for the first time, but there are still some limitations: 1) Security Both the cloud and the security gateway use a unified model architecture regardless of individual IoT model requirements; 2) lack of support for public datasets (the model is random at the beginning), so it can barely detect new or unknown attacks

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