Informer: irregular traffic detection for containerized microservices RPC in the real world
Open accessAbstract
Containerized microservices have been widely deployed in industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Informer extended the analysis of large log datasets with Graph Convolution Networks.
“Informer [64] extended the analysis of large log datasets with Graph Convolution Networks”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [64]
Chen et al. focus their anomaly detection on communication between services (RPCs), trying to automatically identify irregular RPC traffic detection in a simple and practical framework.
“Chen et al. [64] focus their anomaly detection on communication between services (RPCs), trying to automatically identify irregular RPC traffic detection in a simple and practical framework.”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [64]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
(1996) · Cited by 20,179 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
Yu, T., Yin, H.-T., & Zhu, Z.-X. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/505 · Cited by 5,695 · see citing works
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301922 · Cited by 3,123 · see citing works
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845 · see citing works
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingPreprint
Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01926 · Cited by 2,597 · see citing works
Deep Convolutional Networks on Graph-Structured Data
Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255 · see citing works
Deep Neural Networks for Learning Graph Representations
Cao, S., Lu, W., & Xu, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10179 · Cited by 1,070 · see citing works
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal GraphsPreprint
Zhang, J., Shi, X., Xie, J., Ma, H., King, I., & Yeung, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07294 · Cited by 580 · see citing works
A Survey on Similarity Measures in Text Mining
Vijaymeena, M. K., & Kavitha, K. (2016) · Machine Learning and Applications An International Journal · doi:10.5121/mlaij.2016.3103 · Cited by 355 · see citing works
Search-Convolutional Neural NetworksPreprint
Atwood, J. R., & Towsley, D. (2015) · arXiv (Cornell University) · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). · arXiv
- Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network em- bedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1225-1234.
- Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph con- volutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). · arXiv
Cited by
Sorted by citations
Log2Policy: An Approach to Generate Fine-Grained Access Control Rules for Microservices from Scratch
Xu, S., Zhou, Q., Huang, H., Jia, X., Du, H., Chen, Y., & Xie, Y. (2023) · Annual Computer Security Applications Conference · doi:10.1145/3627106.3627137 · Cited by 15 · see citing works
Self-citationSauwens, M., Beni, E. H., Jannes, K., Lagaisse, B., & Joosen, W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91431-8_13 · Cited by 5 · see citing works
Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice
Park, J., Son, J.-G., & Kim, D. (2023) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2023.06.003 · Cited by 3 · see citing works
Zhang, J., & Yang, H. (2026) · Symmetry · doi:10.3390/sym18010087 · Cited by 1 · see citing works
Anomaly detection and root-cause identification in microservices: a survey
Barata, L. M., Sequeira, S., Lopes, E., Inácio, P. R. M., & Freire, M. M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06095-9
“Detecting anomalies and diagnosing their origins in a microservices architecture approach is difficult and requires a lot of time [92].”
Microservice Logs Analysis Employing AI: A Systematic Literature ReviewPreprint
Uddin, M. A., Weerasinghe, S., Gajewski, D., Akbarsharifi, M., Akbarsharifi, R., Stoner, C., Cerny, T., & He, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5768479
- Authors
- Chen, J., Huang, H., & Chen, H.
- Affiliations
- University of California, Davis (US)
- ByteDance (China) (CN)
- Venue
- Proceedings of the 4th ACM/IEEE Symposium on Edge Computing
- Publisher
- ACM
- Published
- 2019-11-07
- Pages
- 389-394
- OpenAlex
- W2985027001
- Cited by
- 6
- References
- 11
- Citations by year
- 2026: 2 · 2025: 1 · 2023: 2 · 2021: 1
- FWCI
- 0.22
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM/IEEE Symposium on Edge Computing (SEC) (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- Army Research Laboratory (W911NF-13-2-0045)
- National Science Foundation (1801751)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
- Keywords
- containerized microservices, microservices security, remote procedure call, unstructured data, security policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
