ARCHIVES
Research Article
A Review on Anomaly Detection using PYOD Package
M. Guhanesvar1
Dr. M. Marimuthu2
1 M.Sc. (integrated) Decision and computing Science, Coimbatore Institute of Technology, Coimbatore, India. 2 Assistant Professor, Department of Computing, Coimbatore Institute of Technology, Coimbatore, India.
Published Online: January-February 2022
Pages: 21-23
Cite this article
No DOIReferences
1. “PyOD: A Python Toolbox for Scalable Outlier Detection” by Yue Zhao, Zain Nasrullah and Zheng Li in Journal of Machine Learning Research (2019)
2. “Anomaly Detection in Finance” by Archana Anandakrishnan, Senthil Kumar, Alexander Statnikov, Tanveer Faruquie and Di Xu in Proceedings of Machine Learning Research(2017)
3. “Churn Prediction in Banking System using K-Means, LOF, and CBLOF” by Irfan Ullah, Hameed Hussain, Iftikhar Ali, and Anum Liaquat in 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
4. “Anomaly Detection using One-Class Neural Networks” by Raghavendra Chalapathy , Aditya Krishna Menon, and Sanjay Chawla in
arXiv:1802.06360 [cs.LG](2018)
5. “Large scale anomaly detection in mixed numerical and categorical input spaces” by CarlosEiras-Franco,DavidMartínez-Rego,BerthaGuijarroBerdiñas,AmparoAlonso-Betanzos,and AntonioBahamonde in Information Sciences Volume 487 June(2019)
6. “Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection” by Xudong Wang, Luis Miranda-Moreno, and Lijun Sun in arXiv:2110.04352 [cs.LG].(2021)
7. “A use case of anomaly detection for identifying unusual water consumption in Jordan” by Samer Nofal, Abdullah Alfarrarjeh, and Amani Abu Jabal in Water Supply Vol 00 No 0, 1 doi: 10.2166/ws.2021.210(2021)
8. “An Improved Parallel Network Traffic Anomaly Detection Method Based on Bagging and GRU” by Xiaoling Tao, Yang Peng ,Feng Zhao, SuFang Wang, and Ziyi Liu in International Conference on Wireless Algorithms, Systems, and Applications(2020)
9. “Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning” by Osama Abdelrahman, and Pantea Keikhosrokiani in IEEE Volume 8 (2020).
10. “Similarity-Measured Isolation Forest: Anomaly Detection Method for Machine Monitoring Data” by C Li, L Guo, H Gao, and Y Li in IEEE Transactions on Instrumentation Volume 70 (2021)
11. “Pattern-Based Contextual Anomaly Detection in HVAC Systems” by Mohsin Munir,Steffen Erkel, Andreas Dengel and Sheraz Ahmed in IEEE International Conference on Data Mining Workshops(2017)
12. “Exploring anomaly detection in systems of systems” by Tommaso Zoppi, Andrea Ceccarelli, and Andrea Bondavalli in SAC '17: Proceedings of the Symposium on Applied Computing (2017)
13. “Autoencoder-based network anomaly detection” by Zhaomin Chen, Chai Kiat Yeo, Bu Sung Lee, and Chiew Tong Lau in Wireless
Telecommunications Symposium (WTS) IEEE(2018)
14. “Semi-supervised anomaly detection algorithms: A comparative summary and future research directions” by Miryam Elizabeth Villa-Pérez, Miguel Á.Álvarez-Carmona, Octavio Loyola-González, Miguel Angel Medina-Pérez, Juan Carlos Velazco-Rossell, and Kim-Kwang Raymond Choo in Knowledge-Based Systems, Volume 218,22 April (2021)
15. “Generative and Autoencoder Models for Large-Scale Multivariate Unsupervised Anomaly Detection” by Nabila Ounasser, Maryem Rhanoui, Mounia Mikram, and Bouchra El Asri in Networking, Intelligent Systems and Security pp 45-58(2021)
16. “Deep Learning for Anomaly Detection: A Survey” by Raghavendra Chalapathy and Sanjay Chawla in arXiv:1901.03407 (cs)(2019)
17. “Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress” by Renjie Wu and Eamonn Keogh in IEEE Transactions on Knowledge and Data Engineering(2021)
18. “Anomaly Detect: An Online Distance-Based Anomaly Detection Algorithm” by Wunjun Huo, Wei Wang and Wen Li in ICWS(2019)
2. “Anomaly Detection in Finance” by Archana Anandakrishnan, Senthil Kumar, Alexander Statnikov, Tanveer Faruquie and Di Xu in Proceedings of Machine Learning Research(2017)
3. “Churn Prediction in Banking System using K-Means, LOF, and CBLOF” by Irfan Ullah, Hameed Hussain, Iftikhar Ali, and Anum Liaquat in 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
4. “Anomaly Detection using One-Class Neural Networks” by Raghavendra Chalapathy , Aditya Krishna Menon, and Sanjay Chawla in
arXiv:1802.06360 [cs.LG](2018)
5. “Large scale anomaly detection in mixed numerical and categorical input spaces” by CarlosEiras-Franco,DavidMartínez-Rego,BerthaGuijarroBerdiñas,AmparoAlonso-Betanzos,and AntonioBahamonde in Information Sciences Volume 487 June(2019)
6. “Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection” by Xudong Wang, Luis Miranda-Moreno, and Lijun Sun in arXiv:2110.04352 [cs.LG].(2021)
7. “A use case of anomaly detection for identifying unusual water consumption in Jordan” by Samer Nofal, Abdullah Alfarrarjeh, and Amani Abu Jabal in Water Supply Vol 00 No 0, 1 doi: 10.2166/ws.2021.210(2021)
8. “An Improved Parallel Network Traffic Anomaly Detection Method Based on Bagging and GRU” by Xiaoling Tao, Yang Peng ,Feng Zhao, SuFang Wang, and Ziyi Liu in International Conference on Wireless Algorithms, Systems, and Applications(2020)
9. “Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning” by Osama Abdelrahman, and Pantea Keikhosrokiani in IEEE Volume 8 (2020).
10. “Similarity-Measured Isolation Forest: Anomaly Detection Method for Machine Monitoring Data” by C Li, L Guo, H Gao, and Y Li in IEEE Transactions on Instrumentation Volume 70 (2021)
11. “Pattern-Based Contextual Anomaly Detection in HVAC Systems” by Mohsin Munir,Steffen Erkel, Andreas Dengel and Sheraz Ahmed in IEEE International Conference on Data Mining Workshops(2017)
12. “Exploring anomaly detection in systems of systems” by Tommaso Zoppi, Andrea Ceccarelli, and Andrea Bondavalli in SAC '17: Proceedings of the Symposium on Applied Computing (2017)
13. “Autoencoder-based network anomaly detection” by Zhaomin Chen, Chai Kiat Yeo, Bu Sung Lee, and Chiew Tong Lau in Wireless
Telecommunications Symposium (WTS) IEEE(2018)
14. “Semi-supervised anomaly detection algorithms: A comparative summary and future research directions” by Miryam Elizabeth Villa-Pérez, Miguel Á.Álvarez-Carmona, Octavio Loyola-González, Miguel Angel Medina-Pérez, Juan Carlos Velazco-Rossell, and Kim-Kwang Raymond Choo in Knowledge-Based Systems, Volume 218,22 April (2021)
15. “Generative and Autoencoder Models for Large-Scale Multivariate Unsupervised Anomaly Detection” by Nabila Ounasser, Maryem Rhanoui, Mounia Mikram, and Bouchra El Asri in Networking, Intelligent Systems and Security pp 45-58(2021)
16. “Deep Learning for Anomaly Detection: A Survey” by Raghavendra Chalapathy and Sanjay Chawla in arXiv:1901.03407 (cs)(2019)
17. “Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress” by Renjie Wu and Eamonn Keogh in IEEE Transactions on Knowledge and Data Engineering(2021)
18. “Anomaly Detect: An Online Distance-Based Anomaly Detection Algorithm” by Wunjun Huo, Wei Wang and Wen Li in ICWS(2019)
Related Articles
2022
A Review on Bamboo Reinforced Concrete Beam
2022
FARMERS AGRICULTURAL PORTAL
2022
Sentiment Analysis of Religious Tweets
2022
Enhancement of beam strength by using bamboo as reinforcement in place of steel bars
2022
Traffic Rule Violation Detection system
2022
