Anomaly detection (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Anomaly detection" in English language version.

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45doi.org

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arxiv.org

Chatterjee, Ayan; Ahmed, Bestoun S. (August 2022). "IoT anomaly detection methods and applications: A survey". Internet of Things. 19: 100568. arXiv:2207.09092. doi:10.1016/j.iot.2022.100568. ISSN 2542-6605. S2CID 250644468.
Hubert, Mia; Debruyne, Michiel; Rousseeuw, Peter J. (2018). "Minimum covariance determinant and extensions". WIREs Computational Statistics. 10 (3). arXiv:1709.07045. doi:10.1002/wics.1421. ISSN 1939-5108. S2CID 67227041.
Zhao, Yue; Nasrullah, Zain; Li, Zheng (2019). "Pyod: A python toolbox for scalable outlier detection" (PDF). Journal of Machine Learning Research. 20. arXiv:1901.01588.

byu.edu

axon.cs.byu.edu

Smith, M. R.; Martinez, T. (2011). "Improving classification accuracy by identifying and removing instances that should be misclassified" (PDF). The 2011 International Joint Conference on Neural Networks. p. 2690. CiteSeerX 10.1.1.221.1371. doi:10.1109/IJCNN.2011.6033571. ISBN 978-1-4244-9635-8. S2CID 5809822.

computer.org

Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (December 2008). "Isolation Forest". 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422. doi:10.1109/ICDM.2008.17. ISBN 9780769535029. S2CID 6505449.

doi.org

Chandola, V.; Banerjee, A.; Kumar, V. (2009). "Anomaly detection: A survey". ACM Computing Surveys. 41 (3): 1–58. doi:10.1145/1541880.1541882. S2CID 207172599.
Thudumu, Srikanth; Branch, Philip; Jin, Jiong; Singh, Jugdutt (Jack) (2020-07-02). "A comprehensive survey of anomaly detection techniques for high dimensional big data". Journal of Big Data. 7 (1): 42. doi:10.1186/s40537-020-00320-x. hdl:10536/DRO/DU:30158643. ISSN 2196-1115.
Kemmerer, R.A.; Vigna, G. (April 2002). "Intrusion detection: a brief history and overview". Computer. 35 (4): supl27–supl30. doi:10.1109/mc.2002.1012428. ISSN 0018-9162.
Denning, D. E. (1987). "An Intrusion-Detection Model" (PDF). IEEE Transactions on Software Engineering. SE-13 (2): 222–232. CiteSeerX 10.1.1.102.5127. doi:10.1109/TSE.1987.232894. S2CID 10028835. Archived (PDF) from the original on June 22, 2015.
Teng, H. S.; Chen, K.; Lu, S. C. (1990). "Adaptive real-time anomaly detection using inductively generated sequential patterns". Proceedings. 1990 IEEE Computer Society Symposium on Research in Security and Privacy (PDF). pp. 278–284. doi:10.1109/RISP.1990.63857. ISBN 978-0-8186-2060-7. S2CID 35632142.
Stojanović, Branka; Božić, Josip; Hofer-Schmitz, Katharina; Nahrgang, Kai; Weber, Andreas; Badii, Atta; Sundaram, Maheshkumar; Jordan, Elliot; Runevic, Joel (January 2021). "Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications". Sensors. 21 (5): 1594. Bibcode:2021Senso..21.1594S. doi:10.3390/s21051594. ISSN 1424-8220. PMC 7956727. PMID 33668773.
Ahmed, Mohiuddin; Mahmood, Abdun Naser; Islam, Md. Rafiqul (February 2016). "A survey of anomaly detection techniques in financial domain". Future Generation Computer Systems. 55: 278–288. doi:10.1016/j.future.2015.01.001. ISSN 0167-739X. S2CID 204982937.
Tomek, Ivan (1976). "An Experiment with the Edited Nearest-Neighbor Rule". IEEE Transactions on Systems, Man, and Cybernetics. 6 (6): 448–452. doi:10.1109/TSMC.1976.4309523.
Smith, M. R.; Martinez, T. (2011). "Improving classification accuracy by identifying and removing instances that should be misclassified" (PDF). The 2011 International Joint Conference on Neural Networks. p. 2690. CiteSeerX 10.1.1.221.1371. doi:10.1109/IJCNN.2011.6033571. ISBN 978-1-4244-9635-8. S2CID 5809822.
Qasim, Maryam; Verdu, Elena (2023-06-01). "Video anomaly detection system using deep convolutional and recurrent models". Results in Engineering. 18: 101026. doi:10.1016/j.rineng.2023.101026. ISSN 2590-1230. S2CID 257728239.
Zhang, Tan; Chowdhery, Aakanksha; Bahl, Paramvir (Victor); Jamieson, Kyle; Banerjee, Suman (2015-09-07). "The Design and Implementation of a Wireless Video Surveillance System". Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. MobiCom '15. New York, NY, USA: Association for Computing Machinery. pp. 426–438. doi:10.1145/2789168.2790123. ISBN 978-1-4503-3619-2. S2CID 12310150.
Gow, Richard; Rabhi, Fethi A.; Venugopal, Srikumar (2018). "Anomaly Detection in Complex Real World Application Systems". IEEE Transactions on Network and Service Management. 15: 83–96. doi:10.1109/TNSM.2017.2771403. hdl:1959.4/unsworks_73660. S2CID 3883483. Retrieved 2023-11-08.
Chatterjee, Ayan; Ahmed, Bestoun S. (August 2022). "IoT anomaly detection methods and applications: A survey". Internet of Things. 19: 100568. arXiv:2207.09092. doi:10.1016/j.iot.2022.100568. ISSN 2542-6605. S2CID 250644468.
Garg, Sahil; Kaur, Kuljeet; Batra, Shalini; Kaddoum, Georges; Kumar, Neeraj; Boukerche, Azzedine (2020-03-01). "A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications". Future Generation Computer Systems. 104: 105–118. doi:10.1016/j.future.2019.09.038. ISSN 0167-739X. S2CID 204077191.
Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra (February 2015). "Anomaly Detection Based on Sensor Data in Petroleum Industry Applications". Sensors. 15 (2): 2774–2797. Bibcode:2015Senso..15.2774M. doi:10.3390/s150202774. ISSN 1424-8220. PMC 4367333. PMID 25633599.
Aljameel, Sumayh S.; Alomari, Dorieh M.; Alismail, Shatha; Khawaher, Fatimah; Alkhudhair, Aljawharah A.; Aljubran, Fatimah; Alzannan, Razan M. (August 2022). "An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning". Computation. 10 (8): 138. doi:10.3390/computation10080138. ISSN 2079-3197.
Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6): e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-09.
Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). "On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study". Data Mining and Knowledge Discovery. 30 (4): 891. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810. S2CID 1952214.
Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases. 8 (3–4): 237–253. CiteSeerX 10.1.1.43.1842. doi:10.1007/s007780050006. S2CID 11707259.
Ramaswamy, S.; Rastogi, R.; Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data – SIGMOD '00. p. 427. doi:10.1145/342009.335437. ISBN 1-58113-217-4.
Angiulli, F.; Pizzuti, C. (2002). Fast Outlier Detection in High Dimensional Spaces. Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 2431. p. 15. doi:10.1007/3-540-45681-3_2. ISBN 978-3-540-44037-6.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4.
Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (December 2008). "Isolation Forest". 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422. doi:10.1109/ICDM.2008.17. ISBN 9780769535029. S2CID 6505449.
Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (March 2012). "Isolation-Based Anomaly Detection". ACM Transactions on Knowledge Discovery from Data. 6 (1): 1–39. doi:10.1145/2133360.2133363. S2CID 207193045.
Schubert, E.; Zimek, A.; Kriegel, H. -P. (2012). "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection". Data Mining and Knowledge Discovery. 28: 190–237. doi:10.1007/s10618-012-0300-z. S2CID 19036098.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. (2009). Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science. Vol. 5476. p. 831. doi:10.1007/978-3-642-01307-2_86. ISBN 978-3-642-01306-5.
Kriegel, H. P.; Kroger, P.; Schubert, E.; Zimek, A. (2012). Outlier Detection in Arbitrarily Oriented Subspaces. 2012 IEEE 12th International Conference on Data Mining. p. 379. doi:10.1109/ICDM.2012.21. ISBN 978-1-4673-4649-8.
Fanaee-T, H.; Gama, J. (2016). "Tensor-based anomaly detection: An interdisciplinary survey". Knowledge-Based Systems. 98: 130–147. doi:10.1016/j.knosys.2016.01.027. S2CID 16368060.
Zimek, A.; Schubert, E.; Kriegel, H.-P. (2012). "A survey on unsupervised outlier detection in high-dimensional numerical data". Statistical Analysis and Data Mining. 5 (5): 363–387. doi:10.1002/sam.11161. S2CID 6724536.
Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. (2001). "Estimating the Support of a High-Dimensional Distribution". Neural Computation. 13 (7): 1443–71. CiteSeerX 10.1.1.4.4106. doi:10.1162/089976601750264965. PMID 11440593. S2CID 2110475.
Hawkins, Simon; He, Hongxing; Williams, Graham; Baxter, Rohan (2002). "Outlier Detection Using Replicator Neural Networks". Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 2454. pp. 170–180. CiteSeerX 10.1.1.12.3366. doi:10.1007/3-540-46145-0_17. ISBN 978-3-540-44123-6. S2CID 6436930.
Hubert, Mia; Debruyne, Michiel; Rousseeuw, Peter J. (2018). "Minimum covariance determinant and extensions". WIREs Computational Statistics. 10 (3). arXiv:1709.07045. doi:10.1002/wics.1421. ISSN 1939-5108. S2CID 67227041.
Hubert, Mia; Debruyne, Michiel (2010). "Minimum covariance determinant". WIREs Computational Statistics. 2 (1): 36–43. doi:10.1002/wics.61. ISSN 1939-0068. S2CID 123086172.
Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J.; Al-Dujaili, Ayad; Duan, Ye; Al-Shamma, Omran; Santamaría, J.; Fadhel, Mohammed A.; Al-Amidie, Muthana; Farhan, Laith (2021-03-31). "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions". Journal of Big Data. 8 (1): 53. doi:10.1186/s40537-021-00444-8. ISSN 2196-1115. PMC 8010506. PMID 33816053.
Belay, Mohammed Ayalew; Blakseth, Sindre Stenen; Rasheed, Adil; Salvo Rossi, Pierluigi (January 2023). "Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions". Sensors. 23 (5): 2844. Bibcode:2023Senso..23.2844B. doi:10.3390/s23052844. ISSN 1424-8220. PMC 10007300. PMID 36905048.
He, Z.; Xu, X.; Deng, S. (2003). "Discovering cluster-based local outliers". Pattern Recognition Letters. 24 (9–10): 1641–1650. Bibcode:2003PaReL..24.1641H. CiteSeerX 10.1.1.20.4242. doi:10.1016/S0167-8655(03)00003-5.
Campello, R. J. G. B.; Moulavi, D.; Zimek, A.; Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection". ACM Transactions on Knowledge Discovery from Data. 10 (1): 5:1–51. doi:10.1145/2733381. S2CID 2887636.
Lazarevic, A.; Kumar, V. (2005). "Feature bagging for outlier detection". Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. pp. 157–166. CiteSeerX 10.1.1.399.425. doi:10.1145/1081870.1081891. ISBN 978-1-59593-135-1. S2CID 2054204.
Nguyen, H. V.; Ang, H. H.; Gopalkrishnan, V. (2010). Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces. Database Systems for Advanced Applications. Lecture Notes in Computer Science. Vol. 5981. p. 368. doi:10.1007/978-3-642-12026-8_29. ISBN 978-3-642-12025-1.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. (2011). Interpreting and Unifying Outlier Scores. Proceedings of the 2011 SIAM International Conference on Data Mining. pp. 13–24. CiteSeerX 10.1.1.232.2719. doi:10.1137/1.9781611972818.2. ISBN 978-0-89871-992-5.
Schubert, E.; Wojdanowski, R.; Zimek, A.; Kriegel, H. P. (2012). On Evaluation of Outlier Rankings and Outlier Scores. Proceedings of the 2012 SIAM International Conference on Data Mining. pp. 1047–1058. doi:10.1137/1.9781611972825.90. ISBN 978-1-61197-232-0.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. (2014). "Ensembles for unsupervised outlier detection". ACM SIGKDD Explorations Newsletter. 15: 11–22. doi:10.1145/2594473.2594476. S2CID 8065347.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. (2014). Data perturbation for outlier detection ensembles. Proceedings of the 26th International Conference on Scientific and Statistical Database Management – SSDBM '14. p. 1. doi:10.1145/2618243.2618257. ISBN 978-1-4503-2722-0.

dx.doi.org

Kemmerer, R.A.; Vigna, G. (April 2002). "Intrusion detection: a brief history and overview". Computer. 35 (4): supl27–supl30. doi:10.1109/mc.2002.1012428. ISSN 0018-9162.
Ahmed, Mohiuddin; Mahmood, Abdun Naser; Islam, Md. Rafiqul (February 2016). "A survey of anomaly detection techniques in financial domain". Future Generation Computer Systems. 55: 278–288. doi:10.1016/j.future.2015.01.001. ISSN 0167-739X. S2CID 204982937.

dtic.mil

apps.dtic.mil

Denning, D. E. (1987). "An Intrusion-Detection Model" (PDF). IEEE Transactions on Software Engineering. SE-13 (2): 222–232. CiteSeerX 10.1.1.102.5127. doi:10.1109/TSE.1987.232894. S2CID 10028835. Archived (PDF) from the original on June 22, 2015.

handle.net

hdl.handle.net

Thudumu, Srikanth; Branch, Philip; Jin, Jiong; Singh, Jugdutt (Jack) (2020-07-02). "A comprehensive survey of anomaly detection techniques for high dimensional big data". Journal of Big Data. 7 (1): 42. doi:10.1186/s40537-020-00320-x. hdl:10536/DRO/DU:30158643. ISSN 2196-1115.
Gow, Richard; Rabhi, Fethi A.; Venugopal, Srikumar (2018). "Anomaly Detection in Complex Real World Application Systems". IEEE Transactions on Network and Service Management. 15: 83–96. doi:10.1109/TNSM.2017.2771403. hdl:1959.4/unsworks_73660. S2CID 3883483. Retrieved 2023-11-08.

harvard.edu

ui.adsabs.harvard.edu

Stojanović, Branka; Božić, Josip; Hofer-Schmitz, Katharina; Nahrgang, Kai; Weber, Andreas; Badii, Atta; Sundaram, Maheshkumar; Jordan, Elliot; Runevic, Joel (January 2021). "Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications". Sensors. 21 (5): 1594. Bibcode:2021Senso..21.1594S. doi:10.3390/s21051594. ISSN 1424-8220. PMC 7956727. PMID 33668773.
Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra (February 2015). "Anomaly Detection Based on Sensor Data in Petroleum Industry Applications". Sensors. 15 (2): 2774–2797. Bibcode:2015Senso..15.2774M. doi:10.3390/s150202774. ISSN 1424-8220. PMC 4367333. PMID 25633599.
Belay, Mohammed Ayalew; Blakseth, Sindre Stenen; Rasheed, Adil; Salvo Rossi, Pierluigi (January 2023). "Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions". Sensors. 23 (5): 2844. Bibcode:2023Senso..23.2844B. doi:10.3390/s23052844. ISSN 1424-8220. PMC 10007300. PMID 36905048.
He, Z.; Xu, X.; Deng, S. (2003). "Discovering cluster-based local outliers". Pattern Recognition Letters. 24 (9–10): 1641–1650. Bibcode:2003PaReL..24.1641H. CiteSeerX 10.1.1.20.4242. doi:10.1016/S0167-8655(03)00003-5.

ieee.org

ieeexplore.ieee.org

Gow, Richard; Rabhi, Fethi A.; Venugopal, Srikumar (2018). "Anomaly Detection in Complex Real World Application Systems". IEEE Transactions on Network and Service Management. 15: 83–96. doi:10.1109/TNSM.2017.2771403. hdl:1959.4/unsworks_73660. S2CID 3883483. Retrieved 2023-11-08.

inesctec.pt

repositorio.inesctec.pt

Fanaee-T, H.; Gama, J. (2016). "Tensor-based anomaly detection: An interdisciplinary survey". Knowledge-Based Systems. 98: 130–147. doi:10.1016/j.knosys.2016.01.027. S2CID 16368060.

jmlr.org

Zhao, Yue; Nasrullah, Zain; Li, Zheng (2019). "Pyod: A python toolbox for scalable outlier detection" (PDF). Journal of Machine Learning Research. 20. arXiv:1901.01588.

lmu.de

dbs.ifi.lmu.de

Anomaly detection benchmark data repository of the Ludwig-Maximilians-Universität München; Mirror Archived 2022-03-31 at the Wayback Machine at University of São Paulo.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4.

nih.gov

pubmed.ncbi.nlm.nih.gov

Stojanović, Branka; Božić, Josip; Hofer-Schmitz, Katharina; Nahrgang, Kai; Weber, Andreas; Badii, Atta; Sundaram, Maheshkumar; Jordan, Elliot; Runevic, Joel (January 2021). "Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications". Sensors. 21 (5): 1594. Bibcode:2021Senso..21.1594S. doi:10.3390/s21051594. ISSN 1424-8220. PMC 7956727. PMID 33668773.
Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra (February 2015). "Anomaly Detection Based on Sensor Data in Petroleum Industry Applications". Sensors. 15 (2): 2774–2797. Bibcode:2015Senso..15.2774M. doi:10.3390/s150202774. ISSN 1424-8220. PMC 4367333. PMID 25633599.
Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. (2001). "Estimating the Support of a High-Dimensional Distribution". Neural Computation. 13 (7): 1443–71. CiteSeerX 10.1.1.4.4106. doi:10.1162/089976601750264965. PMID 11440593. S2CID 2110475.
Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J.; Al-Dujaili, Ayad; Duan, Ye; Al-Shamma, Omran; Santamaría, J.; Fadhel, Mohammed A.; Al-Amidie, Muthana; Farhan, Laith (2021-03-31). "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions". Journal of Big Data. 8 (1): 53. doi:10.1186/s40537-021-00444-8. ISSN 2196-1115. PMC 8010506. PMID 33816053.
Belay, Mohammed Ayalew; Blakseth, Sindre Stenen; Rasheed, Adil; Salvo Rossi, Pierluigi (January 2023). "Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions". Sensors. 23 (5): 2844. Bibcode:2023Senso..23.2844B. doi:10.3390/s23052844. ISSN 1424-8220. PMC 10007300. PMID 36905048.

ncbi.nlm.nih.gov

Stojanović, Branka; Božić, Josip; Hofer-Schmitz, Katharina; Nahrgang, Kai; Weber, Andreas; Badii, Atta; Sundaram, Maheshkumar; Jordan, Elliot; Runevic, Joel (January 2021). "Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications". Sensors. 21 (5): 1594. Bibcode:2021Senso..21.1594S. doi:10.3390/s21051594. ISSN 1424-8220. PMC 7956727. PMID 33668773.
Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra (February 2015). "Anomaly Detection Based on Sensor Data in Petroleum Industry Applications". Sensors. 15 (2): 2774–2797. Bibcode:2015Senso..15.2774M. doi:10.3390/s150202774. ISSN 1424-8220. PMC 4367333. PMID 25633599.
Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J.; Al-Dujaili, Ayad; Duan, Ye; Al-Shamma, Omran; Santamaría, J.; Fadhel, Mohammed A.; Al-Amidie, Muthana; Farhan, Laith (2021-03-31). "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions". Journal of Big Data. 8 (1): 53. doi:10.1186/s40537-021-00444-8. ISSN 2196-1115. PMC 8010506. PMID 33816053.
Belay, Mohammed Ayalew; Blakseth, Sindre Stenen; Rasheed, Adil; Salvo Rossi, Pierluigi (January 2023). "Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions". Sensors. 23 (5): 2844. Bibcode:2023Senso..23.2844B. doi:10.3390/s23052844. ISSN 1424-8220. PMC 10007300. PMID 36905048.

psu.edu

citeseerx.ist.psu.edu

Denning, D. E. (1987). "An Intrusion-Detection Model" (PDF). IEEE Transactions on Software Engineering. SE-13 (2): 222–232. CiteSeerX 10.1.1.102.5127. doi:10.1109/TSE.1987.232894. S2CID 10028835. Archived (PDF) from the original on June 22, 2015.
Jones, Anita K.; Sielken, Robert S. (2000). "Computer System Intrusion Detection: A Survey". Computer Science Technical Report. Department of Computer Science, University of Virginia: 1–25}.
Smith, M. R.; Martinez, T. (2011). "Improving classification accuracy by identifying and removing instances that should be misclassified" (PDF). The 2011 International Joint Conference on Neural Networks. p. 2690. CiteSeerX 10.1.1.221.1371. doi:10.1109/IJCNN.2011.6033571. ISBN 978-1-4244-9635-8. S2CID 5809822.
Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases. 8 (3–4): 237–253. CiteSeerX 10.1.1.43.1842. doi:10.1007/s007780050006. S2CID 11707259.
Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. (2001). "Estimating the Support of a High-Dimensional Distribution". Neural Computation. 13 (7): 1443–71. CiteSeerX 10.1.1.4.4106. doi:10.1162/089976601750264965. PMID 11440593. S2CID 2110475.
Hawkins, Simon; He, Hongxing; Williams, Graham; Baxter, Rohan (2002). "Outlier Detection Using Replicator Neural Networks". Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 2454. pp. 170–180. CiteSeerX 10.1.1.12.3366. doi:10.1007/3-540-46145-0_17. ISBN 978-3-540-44123-6. S2CID 6436930.
He, Z.; Xu, X.; Deng, S. (2003). "Discovering cluster-based local outliers". Pattern Recognition Letters. 24 (9–10): 1641–1650. Bibcode:2003PaReL..24.1641H. CiteSeerX 10.1.1.20.4242. doi:10.1016/S0167-8655(03)00003-5.
Lazarevic, A.; Kumar, V. (2005). "Feature bagging for outlier detection". Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. pp. 157–166. CiteSeerX 10.1.1.399.425. doi:10.1145/1081870.1081891. ISBN 978-1-59593-135-1. S2CID 2054204.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. (2011). Interpreting and Unifying Outlier Scores. Proceedings of the 2011 SIAM International Conference on Data Mining. pp. 13–24. CiteSeerX 10.1.1.232.2719. doi:10.1137/1.9781611972818.2. ISBN 978-0-89871-992-5.

researchgate.net

Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (March 2012). "Isolation-Based Anomaly Detection". ACM Transactions on Knowledge Discovery from Data. 6 (1): 1–39. doi:10.1145/2133360.2133363. S2CID 207193045.
Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautman; Agarwal, Puneet (22–24 April 2015). Long Short Term Memory Networks for Anomaly Detection in Time Series. ESANN 2015: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 89–94. ISBN 978-2-87587-015-5.

sciencedirect.com

Garg, Sahil; Kaur, Kuljeet; Batra, Shalini; Kaddoum, Georges; Kumar, Neeraj; Boukerche, Azzedine (2020-03-01). "A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications". Future Generation Computer Systems. 104: 105–118. doi:10.1016/j.future.2019.09.038. ISSN 0167-739X. S2CID 204077191.

sdu.dk

findresearcher.sdu.dk

Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6): e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-09.

semanticscholar.org

api.semanticscholar.org

Chandola, V.; Banerjee, A.; Kumar, V. (2009). "Anomaly detection: A survey". ACM Computing Surveys. 41 (3): 1–58. doi:10.1145/1541880.1541882. S2CID 207172599.
Denning, D. E. (1987). "An Intrusion-Detection Model" (PDF). IEEE Transactions on Software Engineering. SE-13 (2): 222–232. CiteSeerX 10.1.1.102.5127. doi:10.1109/TSE.1987.232894. S2CID 10028835. Archived (PDF) from the original on June 22, 2015.
Teng, H. S.; Chen, K.; Lu, S. C. (1990). "Adaptive real-time anomaly detection using inductively generated sequential patterns". Proceedings. 1990 IEEE Computer Society Symposium on Research in Security and Privacy (PDF). pp. 278–284. doi:10.1109/RISP.1990.63857. ISBN 978-0-8186-2060-7. S2CID 35632142.
Ahmed, Mohiuddin; Mahmood, Abdun Naser; Islam, Md. Rafiqul (February 2016). "A survey of anomaly detection techniques in financial domain". Future Generation Computer Systems. 55: 278–288. doi:10.1016/j.future.2015.01.001. ISSN 0167-739X. S2CID 204982937.
Smith, M. R.; Martinez, T. (2011). "Improving classification accuracy by identifying and removing instances that should be misclassified" (PDF). The 2011 International Joint Conference on Neural Networks. p. 2690. CiteSeerX 10.1.1.221.1371. doi:10.1109/IJCNN.2011.6033571. ISBN 978-1-4244-9635-8. S2CID 5809822.
Qasim, Maryam; Verdu, Elena (2023-06-01). "Video anomaly detection system using deep convolutional and recurrent models". Results in Engineering. 18: 101026. doi:10.1016/j.rineng.2023.101026. ISSN 2590-1230. S2CID 257728239.
Zhang, Tan; Chowdhery, Aakanksha; Bahl, Paramvir (Victor); Jamieson, Kyle; Banerjee, Suman (2015-09-07). "The Design and Implementation of a Wireless Video Surveillance System". Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. MobiCom '15. New York, NY, USA: Association for Computing Machinery. pp. 426–438. doi:10.1145/2789168.2790123. ISBN 978-1-4503-3619-2. S2CID 12310150.
Gow, Richard; Rabhi, Fethi A.; Venugopal, Srikumar (2018). "Anomaly Detection in Complex Real World Application Systems". IEEE Transactions on Network and Service Management. 15: 83–96. doi:10.1109/TNSM.2017.2771403. hdl:1959.4/unsworks_73660. S2CID 3883483. Retrieved 2023-11-08.
Chatterjee, Ayan; Ahmed, Bestoun S. (August 2022). "IoT anomaly detection methods and applications: A survey". Internet of Things. 19: 100568. arXiv:2207.09092. doi:10.1016/j.iot.2022.100568. ISSN 2542-6605. S2CID 250644468.
Garg, Sahil; Kaur, Kuljeet; Batra, Shalini; Kaddoum, Georges; Kumar, Neeraj; Boukerche, Azzedine (2020-03-01). "A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications". Future Generation Computer Systems. 104: 105–118. doi:10.1016/j.future.2019.09.038. ISSN 0167-739X. S2CID 204077191.
Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6): e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-09.
Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). "On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study". Data Mining and Knowledge Discovery. 30 (4): 891. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810. S2CID 1952214.
Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases. 8 (3–4): 237–253. CiteSeerX 10.1.1.43.1842. doi:10.1007/s007780050006. S2CID 11707259.
Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (December 2008). "Isolation Forest". 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422. doi:10.1109/ICDM.2008.17. ISBN 9780769535029. S2CID 6505449.
Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (March 2012). "Isolation-Based Anomaly Detection". ACM Transactions on Knowledge Discovery from Data. 6 (1): 1–39. doi:10.1145/2133360.2133363. S2CID 207193045.
Schubert, E.; Zimek, A.; Kriegel, H. -P. (2012). "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection". Data Mining and Knowledge Discovery. 28: 190–237. doi:10.1007/s10618-012-0300-z. S2CID 19036098.
Fanaee-T, H.; Gama, J. (2016). "Tensor-based anomaly detection: An interdisciplinary survey". Knowledge-Based Systems. 98: 130–147. doi:10.1016/j.knosys.2016.01.027. S2CID 16368060.
Zimek, A.; Schubert, E.; Kriegel, H.-P. (2012). "A survey on unsupervised outlier detection in high-dimensional numerical data". Statistical Analysis and Data Mining. 5 (5): 363–387. doi:10.1002/sam.11161. S2CID 6724536.
Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. (2001). "Estimating the Support of a High-Dimensional Distribution". Neural Computation. 13 (7): 1443–71. CiteSeerX 10.1.1.4.4106. doi:10.1162/089976601750264965. PMID 11440593. S2CID 2110475.
Hawkins, Simon; He, Hongxing; Williams, Graham; Baxter, Rohan (2002). "Outlier Detection Using Replicator Neural Networks". Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 2454. pp. 170–180. CiteSeerX 10.1.1.12.3366. doi:10.1007/3-540-46145-0_17. ISBN 978-3-540-44123-6. S2CID 6436930.
Hubert, Mia; Debruyne, Michiel; Rousseeuw, Peter J. (2018). "Minimum covariance determinant and extensions". WIREs Computational Statistics. 10 (3). arXiv:1709.07045. doi:10.1002/wics.1421. ISSN 1939-5108. S2CID 67227041.
Hubert, Mia; Debruyne, Michiel (2010). "Minimum covariance determinant". WIREs Computational Statistics. 2 (1): 36–43. doi:10.1002/wics.61. ISSN 1939-0068. S2CID 123086172.
Campello, R. J. G. B.; Moulavi, D.; Zimek, A.; Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection". ACM Transactions on Knowledge Discovery from Data. 10 (1): 5:1–51. doi:10.1145/2733381. S2CID 2887636.
Lazarevic, A.; Kumar, V. (2005). "Feature bagging for outlier detection". Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. pp. 157–166. CiteSeerX 10.1.1.399.425. doi:10.1145/1081870.1081891. ISBN 978-1-59593-135-1. S2CID 2054204.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. (2014). "Ensembles for unsupervised outlier detection". ACM SIGKDD Explorations Newsletter. 15: 11–22. doi:10.1145/2594473.2594476. S2CID 8065347.

snu.ac.kr

dm.snu.ac.kr

An, J.; Cho, S. (2015). "Variational autoencoder based anomaly detection using reconstruction probability" (PDF). Special Lecture on IE. 2 (1): 1–18. SNUDM-TR-2015-03.

ucl.ac.uk

discovery.ucl.ac.uk

Zhang, Tan; Chowdhery, Aakanksha; Bahl, Paramvir (Victor); Jamieson, Kyle; Banerjee, Suman (2015-09-07). "The Design and Implementation of a Wireless Video Surveillance System". Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. MobiCom '15. New York, NY, USA: Association for Computing Machinery. pp. 426–438. doi:10.1145/2789168.2790123. ISBN 978-1-4503-3619-2. S2CID 12310150.

unc.edu

cs.unc.edu

Teng, H. S.; Chen, K.; Lu, S. C. (1990). "Adaptive real-time anomaly detection using inductively generated sequential patterns". Proceedings. 1990 IEEE Computer Society Symposium on Research in Security and Privacy (PDF). pp. 278–284. doi:10.1109/RISP.1990.63857. ISBN 978-0-8186-2060-7. S2CID 35632142.

usp.br

lapad-web.icmc.usp.br

Anomaly detection benchmark data repository of the Ludwig-Maximilians-Universität München; Mirror Archived 2022-03-31 at the Wayback Machine at University of São Paulo.

web.archive.org

Denning, D. E. (1987). "An Intrusion-Detection Model" (PDF). IEEE Transactions on Software Engineering. SE-13 (2): 222–232. CiteSeerX 10.1.1.102.5127. doi:10.1109/TSE.1987.232894. S2CID 10028835. Archived (PDF) from the original on June 22, 2015.
Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6): e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-09.
Anomaly detection benchmark data repository of the Ludwig-Maximilians-Universität München; Mirror Archived 2022-03-31 at the Wayback Machine at University of São Paulo.

wiley.com

onlinelibrary.wiley.com

Hubert, Mia; Debruyne, Michiel (2010). "Minimum covariance determinant". WIREs Computational Statistics. 2 (1): 36–43. doi:10.1002/wics.61. ISSN 1939-0068. S2CID 123086172.

wolfram.com

reference.wolfram.com

"FindAnomalies". Mathematica documentation.

worldcat.org

Hawkins, Douglas M. (1980). Identification of Outliers. Springer. ISBN 978-0-412-21900-9. OCLC 6912274.
Barnett, Vic; Lewis, Lewis (1978). Outliers in statistical data. Wiley. ISBN 978-0-471-99599-9. OCLC 1150938591.
Thudumu, Srikanth; Branch, Philip; Jin, Jiong; Singh, Jugdutt (Jack) (2020-07-02). "A comprehensive survey of anomaly detection techniques for high dimensional big data". Journal of Big Data. 7 (1): 42. doi:10.1186/s40537-020-00320-x. hdl:10536/DRO/DU:30158643. ISSN 2196-1115.
Kemmerer, R.A.; Vigna, G. (April 2002). "Intrusion detection: a brief history and overview". Computer. 35 (4): supl27–supl30. doi:10.1109/mc.2002.1012428. ISSN 0018-9162.
Stojanović, Branka; Božić, Josip; Hofer-Schmitz, Katharina; Nahrgang, Kai; Weber, Andreas; Badii, Atta; Sundaram, Maheshkumar; Jordan, Elliot; Runevic, Joel (January 2021). "Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications". Sensors. 21 (5): 1594. Bibcode:2021Senso..21.1594S. doi:10.3390/s21051594. ISSN 1424-8220. PMC 7956727. PMID 33668773.
Ahmed, Mohiuddin; Mahmood, Abdun Naser; Islam, Md. Rafiqul (February 2016). "A survey of anomaly detection techniques in financial domain". Future Generation Computer Systems. 55: 278–288. doi:10.1016/j.future.2015.01.001. ISSN 0167-739X. S2CID 204982937.
Qasim, Maryam; Verdu, Elena (2023-06-01). "Video anomaly detection system using deep convolutional and recurrent models". Results in Engineering. 18: 101026. doi:10.1016/j.rineng.2023.101026. ISSN 2590-1230. S2CID 257728239.
Chatterjee, Ayan; Ahmed, Bestoun S. (August 2022). "IoT anomaly detection methods and applications: A survey". Internet of Things. 19: 100568. arXiv:2207.09092. doi:10.1016/j.iot.2022.100568. ISSN 2542-6605. S2CID 250644468.
Garg, Sahil; Kaur, Kuljeet; Batra, Shalini; Kaddoum, Georges; Kumar, Neeraj; Boukerche, Azzedine (2020-03-01). "A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications". Future Generation Computer Systems. 104: 105–118. doi:10.1016/j.future.2019.09.038. ISSN 0167-739X. S2CID 204077191.
Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra (February 2015). "Anomaly Detection Based on Sensor Data in Petroleum Industry Applications". Sensors. 15 (2): 2774–2797. Bibcode:2015Senso..15.2774M. doi:10.3390/s150202774. ISSN 1424-8220. PMC 4367333. PMID 25633599.
Aljameel, Sumayh S.; Alomari, Dorieh M.; Alismail, Shatha; Khawaher, Fatimah; Alkhudhair, Aljawharah A.; Aljubran, Fatimah; Alzannan, Razan M. (August 2022). "An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning". Computation. 10 (8): 138. doi:10.3390/computation10080138. ISSN 2079-3197.
Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6): e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-09.
Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). "On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study". Data Mining and Knowledge Discovery. 30 (4): 891. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810. S2CID 1952214.
Hubert, Mia; Debruyne, Michiel; Rousseeuw, Peter J. (2018). "Minimum covariance determinant and extensions". WIREs Computational Statistics. 10 (3). arXiv:1709.07045. doi:10.1002/wics.1421. ISSN 1939-5108. S2CID 67227041.
Hubert, Mia; Debruyne, Michiel (2010). "Minimum covariance determinant". WIREs Computational Statistics. 2 (1): 36–43. doi:10.1002/wics.61. ISSN 1939-0068. S2CID 123086172.
Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J.; Al-Dujaili, Ayad; Duan, Ye; Al-Shamma, Omran; Santamaría, J.; Fadhel, Mohammed A.; Al-Amidie, Muthana; Farhan, Laith (2021-03-31). "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions". Journal of Big Data. 8 (1): 53. doi:10.1186/s40537-021-00444-8. ISSN 2196-1115. PMC 8010506. PMID 33816053.
Belay, Mohammed Ayalew; Blakseth, Sindre Stenen; Rasheed, Adil; Salvo Rossi, Pierluigi (January 2023). "Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions". Sensors. 23 (5): 2844. Bibcode:2023Senso..23.2844B. doi:10.3390/s23052844. ISSN 1424-8220. PMC 10007300. PMID 36905048.