Abstract
With the rapid development of the Internet of Things (IoT), the security problem of IoT is becoming increasingly prominent. Deep learning (DL) has achieved success in network intrusion detection systems (NIDS) for IoT. Its capability of automatically extracting high-dimensional features from data and finding the association between data make it easy to identify abnormal activity from network traffic. However, DL method requires a large amount of labeled data, which is very time-consuming and expensive. Due to the privacy of IoT data, it is hard to collect enough data to train models. Also, the heterogeneity of IoT makes the NID model trained from the data collected from one IoT unable to be directly applied to another one. To address the problem, domain adaptation (DA) has been used by transferring the knowledge from the domain with huge amounts of labeled data to the domain with less or unlabeled data. However, previous DA methods generally assume the same label spaces between source and target domain, which is not feasible in a complex real environment of IoT. In this paper, we propose a NID framework using a weighted adversarial nets-based partial domain adaptation method to address this problem of inconsistent label spaces by mapping two domains to a domain-invariant feature space. The proposal can train a highly accurate NID model through the knowledge transfer from the abundant public labeled dataset of the traditional Internet to the unlabeled dataset of IoT. In addition, the proposed scheme can detect unknown attacks in the IoT with the help of knowledge from the traditional Internet. Moreover, the proposed scheme is an online NID detection which is more suitable for real IoT application. The experiments results demonstrate that our proposed scheme can achieve a good performance to detect attacks.
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Notes
- 1.
Our code is public available at https://github.com/rainforest2378/IoT-PDA.git.
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Acknowledgment
This work is supported by the Project of Beijing Municipal Science & Technology Plan under Grant No. Z191100007119001 and Z191100007119003.
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Fan, Y., Li, Y., Cui, H., Yang, H., Zhang, Y., Wang, W. (2021). An Intrusion Detection Framework for IoT Using Partial Domain Adaptation. In: Lu, W., Sun, K., Yung, M., Liu, F. (eds) Science of Cyber Security. SciSec 2021. Lecture Notes in Computer Science(), vol 13005. Springer, Cham. https://doi.org/10.1007/978-3-030-89137-4_3
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