Abstract
Anomaly detection is a crucial and challenging subject that has been studied within diverse research areas. In this work, we focus on log data (especially computer system logs) which is a valuable source to investigate system status and detect system abnormality. In order to capture transition pattern and position information of records in logs simultaneously, we transfer log files to session graphs and formulate the log anomaly detection problem as a graph classification task. Specifically, we propose GLAD-PAW, a graph-based log anomaly detection model utilizing a new position aware weighted graph attention layer (PAWGAT) and a global attention readout function to learn embeddings of records and session graphs. Extensive experimental studies demonstrate that our proposed model outperforms existing log anomaly detection methods including both statistical and deep learning approaches.
Y. Wan and Y. Liu— Equal contribution.
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References
Cangea, C., Veličković, P., Jovanović, N., Kipf, T., Liò, P.: Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287 (2018)
Diehl, F.: Edge contraction pooling for graph neural networks. arXiv preprint arXiv:1905.10990 (2019)
Du, M., Li, F., Zheng, G., Srikumar, V.: Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1285–1298 (2017)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1263–1272 (2017)
He, P., Zhu, J., Zheng, Z., Lyu, M.R.: Drain: An online log parsing approach with fixed depth tree. In: Proceedings of the IEEE International Conference on Web Services, pp. 33–40 (2017)
He, S., Zhu, J., He, P., Lyu, M.R.: Experience report: System log analysis for anomaly detection. In: Proceedings of the 27th International Symposium on Software Reliability Engineering, pp. 207–218 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (ICLR) (2017)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. In: 4th International Conference on Learning Representations (ICLR) (2016)
Lin, Q., Zhang, H., Lou, J.G., Zhang, Y., Chen, X.: Log clustering based problem identification for online service systems. In: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), pp. 102–111. IEEE (2016)
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008)
Lou, J.G., Fu, Q., Yang, S., Xu, Y., Li, J.: Mining invariants from console logs for system problem detection. In: USENIX Annual Technical Conference, pp. 1–14 (2010)
Meng, W., et al.: Loganomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4739–4745 (2019)
Pham, T., Tran, T., Dam, H., Venkatesh, S.: Graph classification via deep learning with virtual nodes. arXiv preprint arXiv:1708.04357 (2017)
Song, Y., Keromytis, A.D., Stolfo, S.J.: Spectrogram: a mixture-of-markov-chains model for anomaly detection in web traffic. In: NDSS (2009)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations (ICLR) (2018)
Wang, J., Xu, Q., Lei, J., Lin, C., Xiao, B.: Pa-ggan: session-based recommendation with position-aware gated graph attention network. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2020)
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 2048–2057 (2015)
Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.: Largescale system problem detection by mining console logs. In: Proceedings of SOSP 2009 (2009)
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, pp. 4800–4810 (2018)
Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: AAAI, vol. 18, pp. 4438–4445 (2018)
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Wan, Y., Liu, Y., Wang, D., Wen, Y. (2021). GLAD-PAW: Graph-Based Log Anomaly Detection by Position Aware Weighted Graph Attention Network. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_6
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DOI: https://doi.org/10.1007/978-3-030-75762-5_6
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