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Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention
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“Logs are a crucial source of information in software systems, capturing system runtime behavior and aiding in software engineering tasks like program comprehension [1], [2], anomaly detection [3]- [6], and failure diagnosis [7], [8].”
From Practitioners’ expectations on log anomaly detection (2025) · cites this paper as [5]
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“industrial datasets are often access-restricted due to security and privacy concerns, as highlighted by Lee et al. [5].”
From Practitioners’ expectations on log anomaly detection (2025) · cites this paper as Lee et al. [5]
In the citing paper
“publicly available data is typically single-sourced”
From Practitioners’ expectations on log anomaly detection (2025) · cites this paper as Lee et al. [5]
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Cites
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Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330672 · Cited by 1,804
Self-citationUSAD: UnSupervised Anomaly Detection on Multivariate Time Series
Audibert, J., Michiardi, P., Guyard, F., Marti, S., & Zuluaga, M. A. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403392 · Cited by 1,094
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Self-citationTools and benchmarks for automated log parsing
Zhu, J., He, S., Liu, J., He, P., Xie, Q., Zheng, Z., & Lyu, M. R. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip.2019.00021 · Cited by 574
Self-citationLi, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., & Pei, D. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467075 · Cited by 464
Self-citationLog-based Anomaly Detection Without Log Parsing
Le, V.-H., & Zhang, H. (2021) · 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678773 · Cited by 361 · cited 2×
“Many efforts have been devoted to automated anomaly detection for large-scale system reliability insurance based on logs and metrics [46]–[53].”
Towards Automated Log Parsing for Large-Scale Log Data Analysis
He, P., Zhu, J., He, S., Li, J., & Lyu, M. R. (2018) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2762673 · Cited by 230
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Cited by
Sorted by citations
Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data
Lee, C., Yang, T., Chen, Z., Su, Y., & Lyu, M. R. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00150 · Cited by 149
Self-citationTwin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System
Huang, J., Yang, Y., Yu, H., Li, J., & Zheng, X. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00138 · Cited by 50
Self-citationPractitioners’ expectations on log anomaly detection
Ma, X., Li, Y., Keung, J., Yu, X., Zou, H., Yang, Z., Sarro, F., & Barr, E. T. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3586700 · Cited by 21
“Moreover, publicly available data is typically single-sourced, and industrial datasets are often access-restricted due to security and privacy concerns, as highlighted by Lee et al. [5].”
AIOpsLab in Action: An Open Platform for AIOps Research
Ma, M., Clark, J., & Zhang, S. (2025) · Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering · doi:10.1145/3696630.3728619
Metadata
- Authors
- Lee, C., Yang, T., Chen, Z., Su, Y., Yang, Y., & Lyu, M. R.
- Venue
- 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
- Publisher
- IEEE
- Published
- 2023-05
- Pages
- 1724-1736
- Cited by
- 68
- References
- 9
- Funders
- National Natural Science Foundation of China (62202511)
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- CoChat. (2026). Citation check: Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention. https://app.cochat.ai/papers/10.1109/icse48619.2023.00148
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- Lee, C., Yang, T., Chen, Z., Su, Y., Yang, Y., & Lyu, M. R. (2023). Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). https://doi.org/10.1109/icse48619.2023.00148
