Data security: Based on the "data-centric security system" position, it generally refers to the entire security system focusing on data classification and the protection of the entire life cycle of sensitive data.
DataRisk Detection: DataRisk Detection identification is a way to identify possible causes of harm to confidentiality, integrity, availability, etc. of data assets in data processing activities, and to further analyze the threat motive, frequency and possibility of occurrence.
This repository collects:
Books & Academic Papers
Online Courses and Videos
DataRisk Datasets
Open-source and Commercial Libraries/Toolkits
Key Conferences & Journals
More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ ([email protected]). Enjoy reading!
Outlier Analysis by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. A must-read for people in the field of outlier detection. [Preview.pdf]
Udemy Outlier Detection Algorithms in Data Mining and Data Science: [See Video]
Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques: [See Video]
3. Toolbox & Datasets
3.1. Multivariate Data
[Python] Python Outlier Detection (PyOD): PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.
[Python] Python Streaming Anomaly Detection (PySAD): PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.
[Python] Scalable Unsupervised Outlier Detection (SUOD): SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.
[Java] RapidMiner Anomaly Detection Extension: The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.
[Python] TODS: TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
[Python] skyline: Skyline is a near real time anomaly detection system.
[Python] banpei: Banpei is a Python package of the anomaly detection.
[Python] telemanom: A framework for using LSTMs to detect anomalies in multivariate time series data.
[Python] DeepADoTS: A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.
[Python] NAB: The Numenta Anomaly Benchmark: NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.
[Python] CueObserve: Anomaly detection on SQL data warehouses and databases.
[R] AnomalyDetection: AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.
[R] anomalize: The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
Abe, N., Zadrozny, B. and Langford, J., 2006, August. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 504-509, ACM.
Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, pp.278-288.
Akoglu, L., Tong, H. and Koutra, D., 2015. Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3), pp.626-688.
Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In European Conference on Principles of Data Mining and Knowledge Discovery, pp. 15-27.
Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA).
Bandaragoda, Tharindu R., Kai Ming Ting, David Albrecht, Fei Tony Liu, Ye Zhu, and Jonathan R. Wells. "Isolation‐based anomaly detection using nearest‐neighbor ensembles." Computational Intelligence 34, no. 4 (2018): 968-998.
Bhatia, S., Hooi, B., Yoon, M., Shin, K. and Faloutsos. C., 2020. MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. In AAAI Conference on Artificial Intelligence (AAAI).
Bulusu, S., Kailkhura, B., Li, B., Varshney, P. and Song, D., 2020. Anomalous instance detection in deep learning: A survey (No. LLNL-CONF-808677). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).
Campos, G.O., Zimek, A., Sander, J., Campello, R.J., Micenková, B., Schubert, E., Assent, I. and Houle, M.E., 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), pp.891-927.
Campos, G.O., Zimek, A. and Meira, W., 2018, June. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 564-576). Springer, Cham.
Campos, G.O., Moreira, E., Meira Jr, W. and Zimek, A., 2019. Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. Computer Science & Information Systems, 16(2).
Castellani, A., Schmitt, S., Squartini, S., 2020. Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning. In IEEE Transactions on Industrial Informatics.
Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. SIAM International Conference on Data Mining, pp. 90-98. Society for Industrial and Applied Mathematics.
Dang, X.H., Assent, I., Ng, R.T., Zimek, A. and Schubert, E., 2014, March. Discriminative features for identifying and interpreting outliers. In International Conference on Data Engineering (ICDE). IEEE.
Davidson, I. and Ravi, S.S., 2020. A framework for determining the fairness of outlier detection. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI2020) (Vol. 2029).
Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM.
Djenouri, Y. and Zimek, A., 2018, June. Outlier detection in urban traffic data. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. ACM.
Domingues, R., Filippone, M., Michiardi, P. and Zouaoui, J., 2018. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, pp.406-421.
Falcão, F., Zoppi, T., Silva, C.B.V., Santos, A., Fonseca, B., Ceccarelli, A. and Bondavalli, A., 2019, April. Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, (pp. 318-327). ACM.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G. and Vázquez, E., 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), pp.18-28.
Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4), p.e0152173.
Gupta, M., Gao, J., Aggarwal, C.C. and Han, J., 2014. Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), pp.2250-2267.
Hendrycks, D., Mazeika, M. and Dietterich, T.G., 2019. Deep Anomaly Detection with Outlier Exposure. International Conference on Learning Representations (ICLR).
Hodge, V. and Austin, J., 2004. A survey of outlier detection methodologies. Artificial intelligence review, 22(2), pp.85-126.
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(1, 2) Hundman, K., Constantinou, V., Laporte, C., Colwell, I. and Soderstrom, T., 2018, July. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 387-395). ACM.
Kannan, R., Woo, H., Aggarwal, C.C. and Park, H., 2017, June. Outlier detection for text data. In Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 489-497. Society for Industrial and Applied Mathematics.
Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. Tutorial at ACM SIGKDD 2010.
[39]
(1, 2) Lai, K.H., Zha, D., Xu, J., Zhao, Y., Wang, G. and Hu, X., 2021. Revisiting Time Series Outlier Detection: Definitions and Benchmarks. Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.
Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 612-620. Society for Industrial and Applied Mathematics.
Lavin, A. and Ahmad, S., 2015, December. Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 38-44). IEEE.
Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. Tutorial at ECML PKDD 2008.
Li, D., Chen, D., Jin, B., Shi, L., Goh, J. and Ng, S.K., 2019, September. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks (pp. 703-716). Springer, Cham.
Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In International Joint Conference on Artificial Intelligence (IJCAI-18), pp.2461-2467.
Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative Adversarial Active Learning for Unsupervised Outlier Detection. IEEE transactions on knowledge and data engineering.
Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H. and Hu, X., 2020. AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning. ICDE.
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(1, 2) Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q.Z., Xiong, H. and Akoglu, L., 2021. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering.
Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480.
Manzoor, E., Lamba, H. and Akoglu, L. Outlier Detection in Feature-Evolving Data Streams. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018.
Pang, G., Cao, L., Chen, L. and Liu, H., 2016, December. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 410-419). IEEE.
Pang, G., Cao, L., Chen, L. and Liu, H., 2017, August. Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 2585-2591). AAAI Press.
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(1, 2) Pang, G., Cao, L., Chen, L. and Liu, H., 2018. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018.
Pelleg, D. and Moore, A.W., 2005. Active learning for anomaly and rare-category detection. In Advances in neural information processing systems, pp. 1073-1080.
Radovanović, M., Nanopoulos, A. and Ivanović, M., 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE transactions on knowledge and data engineering, 27(5), pp.1369-1382.
Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record, 29(2), pp. 427-438.
Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J. and Zhang, Q., 2019. Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.
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Yoon, S., Lee, J. G., & Lee, B. S., 2020. Ultrafast local outlier detection from a data stream with stationary region skipping. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1181-1191)
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