Abstract
The huge amount of data generated by sensor networks enables many potential analyses. However, one important limiting factor for the analyses of sensor data is the possible presence of anomalies, which may affect the validity of any conclusion we could draw. This aspect motivates the adoption of a preliminary anomaly detection method. Existing methods usually do not consider the spatial nature of data generated by sensor networks. Properly modeling the spatial nature of the data, by explicitly considering spatial autocorrelation phenomena, has the potential to highlight the degree of agreement or disagreement of multiple sensor measurements located in different geographical positions. The intuition is that one could improve anomaly detection performance by considering the spatial context. In this paper, we propose a spatially-aware anomaly detection method based on a stacked auto-encoder architecture. Specifically, the proposed architecture includes a specific encoding stage that models the spatial autocorrelation in data observed at different locations. Finally, a distance-based approach leverages the embedding features returned by the auto-encoder to identify possible anomalies. Our experimental evaluation on real-world geo-distributed data collected from renewable energy plants shows the effectiveness of the proposed method, also when compared to state-of-the-art anomaly detection methods.
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References
Aggarwal, C.C.: Outlier ensembles: position paper. ACM SIGKDD Explor. Newsl 14(2), 49–58 (2013)
Anselin, L.: Local indicators of spatial association-LISA. Geograph. Anal. 27(2), 93–115 (1995)
Beggel, L., Pfeiffer, M., Bischl, B.: Robust anomaly detection in images using adversarial autoencoders. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11906, pp. 206–222. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46150-8_13
Bengio, Y.: Learning Deep Architectures for AI. Now Publishers Inc. (2009)
Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 437–478. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_26
Ceci, M., Corizzo, R., Fumarola, F., Malerba, D., Rashkovska, A.: Predictive modeling of pv energy production: How to set up the learning task for a better prediction? IEEE Trans. Ind. Inf. 13(3), 956–966 (2016)
Ceci, M., Corizzo, R., Japkowicz, N., Mignone, P., Pio, G.: Echad: embedding-based change detection from multivariate time series in smart grids. IEEE Access 8, 156053–156066 (2020)
Ceci, M., Corizzo, R., Malerba, D., Rashkovska, A.: Spatial autocorrelation and entropy for renewable energy forecasting. Data Min. Knowl. Disc. 33(3), 698–729 (2019). https://doi.org/10.1007/s10618-018-0605-7
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
Corizzo, R., Ceci, M., Japkowicz, N.: Anomaly detection and repair for accurate predictions in geo-distributed big data. Big Data Res. 16, 18–35 (2019)
Corizzo, R., Pio, G., Ceci, M., Malerba, D.: DENCAST: distributed density-based clustering for multi-target regression. J. Big Data 6(1), 1–27 (2019). https://doi.org/10.1186/s40537-019-0207-2
Kou, Y., Lu, C.T., Chen, D.: Spatial weighted outlier detection. In: Proceedings of SIAM International Conference on Data Mining 2006, pp. 614–618. SIAM (2006)
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)
Liu, T., Moore, A.W., Gray, A., Yang, K.: An investigation of practical approximate nearest neighbor algorithms. In: NIPS 2004, pp. 825–832. MIT Press (2004)
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, pp. 582–588 (2000)
Shekhar, S., Lu, C.T., Zhang, P.: Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In: ACM SIGKDD, pp. 371–376 (2001)
Stojanova, D., Ceci, M., Appice, A., Malerba, D., Džeroski, S.: Dealing with spatial autocorrelation when learning predictive clustering trees. Ecol. Inf. 13, 22–39 (2013)
Xing, H.J., Liu, W.T.: Robust adaboost based ensemble of one-class support vector machines. Inf. Fusion 55, 45–58 (2020)
Zhang, C., Song, D., Chen, Y., Feng, X., et al.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: AAAI 2019, vol. 33, pp. 1409–1416 (2019)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: ACM SIGKDD 2017, pp. 665–674 (2017)
Acknowledgement
The authors acknowledge the support of the U.S. DARPA through the project “Lifelong Streaming Anomaly Detection” (Grant N. A19-0131-003 and A21-0113-002), and of the EU Commission through the H2020 project “IMPETUS-Intelligent Management of Processes, Ethics and Technology for Urban Safety” (Grant n. 883286). GP acknowledges the support of Ministry of Universities and Research (MUR) through the project “Big Data Analytics”, AIM 1852414, activity 1, line 1. PM acknowledges the support of Apulia Region through the project “Metodi per l’ottimizzazione delle reti di distribuzione di energia e per la pianificazione di interventi manutentivi ed evolutivi” (CUP H94I20000410008, Grant n. 7EDD092A) in the context of “Research for Innovation - REFIN”. We also acknowledge the support of NVIDIA through the donation of a Titan V GPU.
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Corizzo, R., Ceci, M., Pio, G., Mignone, P., Japkowicz, N. (2021). Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_36
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