publication . Preprint . 2018

Active Anomaly Detection via Ensembles

Das, Shubhomoy; Islam, Md Rakibul; Jayakodi, Nitthilan Kannappan; Doppa, Janardhan Rao;
Open Access English
  • Published: 17 Sep 2018
Abstract
In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives. One important way to configure the anomaly detector is by providing true labels for a few instances. We study the problem of label-efficient active learning to automatically tune anomaly detection ensembles and make four main contributions. First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning. This insight allows us to relate the greedy querying strategy to uncertainty sampling, with implications for label-e...
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
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40 references, page 1 of 3

[Aggarwal and Sathe 2017] Aggarwal, C. C., and Sathe, S.

2017. Outlier Ensembles. Springer.

[Balcan and Feldman 2015] Balcan, M. F., and Feldman, V.

2015. Statistical active learning algorithms for noise tolerance and differential privacy. Algorithmica 72(1):282-315.

[Balcan, Broder, and Zhang 2007] Balcan, M.-F.; Broder, A. Z.; and Zhang, T. 2007. Margin based active learning. In COLT. [OpenAIRE]

[Breunig et al. 2000] Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; and Sander, J. 2000. Lof: Identifying density-based local outliers. In ACM SIGMOD International Conference on Management of Data. [OpenAIRE]

[Chandola, Banerjee, and Kumar 2009] Chandola, V.; Banerjee, A.; and Kumar, V. 2009. Anomaly detection: A survey.

ACM Computing Surveys 41(3):158.

[Chen et al. 2017] Chen, J.; Sathe, S.; Aggarwal, C.; and Turaga, D. 2017. Outlier detection with autoencoder ensembles. In SIAM International Conference on Data Mining. [OpenAIRE]

[Cohn, Atlas, and Ladner 1994] Cohn, D.; Atlas, L.; and Ladner, R. 1994. Improving generalization with active learning.

Machine Learning 15(2):201-221.

[Das et al. 2016] Das, S.; Wong, W.-K.; Dietterich, T. G.; Fern, A.; and Emmott, A. 2016. Incorporating expert feedback into active anomaly discovery. In IEEE ICDM.

[Das et al. 2017] Das, S.; Wong, W.-K.; Fern, A.; Dietterich, T. G.; and Siddiqui, M. A. 2017. Incorporating expert feedback into tree-based anomaly detection. In KDD IDEA Workshop.

[Dasgupta, Kalai, and Monteleoni 2009] Dasgupta, S.; Kalai, A. T.; and Monteleoni, C. 2009. Analysis of perceptron-based active learning. JMLR 10:281-299.

[Ditzler and Polikar 2013] Ditzler, G., and Polikar, R. 2013.

40 references, page 1 of 3
Abstract
In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives. One important way to configure the anomaly detector is by providing true labels for a few instances. We study the problem of label-efficient active learning to automatically tune anomaly detection ensembles and make four main contributions. First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning. This insight allows us to relate the greedy querying strategy to uncertainty sampling, with implications for label-e...
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Download from
40 references, page 1 of 3

[Aggarwal and Sathe 2017] Aggarwal, C. C., and Sathe, S.

2017. Outlier Ensembles. Springer.

[Balcan and Feldman 2015] Balcan, M. F., and Feldman, V.

2015. Statistical active learning algorithms for noise tolerance and differential privacy. Algorithmica 72(1):282-315.

[Balcan, Broder, and Zhang 2007] Balcan, M.-F.; Broder, A. Z.; and Zhang, T. 2007. Margin based active learning. In COLT. [OpenAIRE]

[Breunig et al. 2000] Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; and Sander, J. 2000. Lof: Identifying density-based local outliers. In ACM SIGMOD International Conference on Management of Data. [OpenAIRE]

[Chandola, Banerjee, and Kumar 2009] Chandola, V.; Banerjee, A.; and Kumar, V. 2009. Anomaly detection: A survey.

ACM Computing Surveys 41(3):158.

[Chen et al. 2017] Chen, J.; Sathe, S.; Aggarwal, C.; and Turaga, D. 2017. Outlier detection with autoencoder ensembles. In SIAM International Conference on Data Mining. [OpenAIRE]

[Cohn, Atlas, and Ladner 1994] Cohn, D.; Atlas, L.; and Ladner, R. 1994. Improving generalization with active learning.

Machine Learning 15(2):201-221.

[Das et al. 2016] Das, S.; Wong, W.-K.; Dietterich, T. G.; Fern, A.; and Emmott, A. 2016. Incorporating expert feedback into active anomaly discovery. In IEEE ICDM.

[Das et al. 2017] Das, S.; Wong, W.-K.; Fern, A.; Dietterich, T. G.; and Siddiqui, M. A. 2017. Incorporating expert feedback into tree-based anomaly detection. In KDD IDEA Workshop.

[Dasgupta, Kalai, and Monteleoni 2009] Dasgupta, S.; Kalai, A. T.; and Monteleoni, C. 2009. Analysis of perceptron-based active learning. JMLR 10:281-299.

[Ditzler and Polikar 2013] Ditzler, G., and Polikar, R. 2013.

40 references, page 1 of 3
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