"Using unsupervised ML for anomaly detection"

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Book	Author(s)	Year	Publisher
Traffic Anomaly Detection	Cuadra-Sánchez Antonio and Javier Aracil-Rico	2016	Elsevier
Anomaly Detection Principles and Algorithms	Kishan G. Mehrotra; Chilukuri K. Mohan; HuaMing Huang	1st ed. 2017	Springer International Publishing
Anomaly Detection: Techniques and Applications	Saira Banu, Shriram Raghunathan, Dinesh Mavaluru, A. Syed Mustafa	2021	Nova Science
Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch	Sridhar Alla; Suman Kalyan Adari	1st ed.	Apress
empirical comparison of monitoring algorithms for access anomaly detection	Anne Dinning	1989	Courant Institute of Mathematical Sciences, New York University
An evaluation of monitoring algorithms for access anomaly detection	Anne Dinning	1989	Courant Institute of Mathematical Sciences, New York University
An evaluation of monitoring algorithms for access anomaly detection	Anne Dinning	1989	Courant Institute of Mathematical Sciences, New York University
Functional programming using standard ML	Åke Wikström	1987	Prentice Hall
Network Anomaly Detection: A Machine Learning Perspective	Dhruba Kumar Bhattacharyya; Jugal Kumar Kalita	2013	Routledge
Anomaly-Detection and Health-Analysis Techniques for Core Router Systems	Shi Jin; Zhaobo Zhang; Krishnendu Chakrabarty; Xinli Gu	1st ed. 2020	Springer International Publishing
Control Charts and Machine Learning for Anomaly Detection in Manufacturing	Kim Phuc Tran	1st ed. 2022	Springer International Publishing
Biologically Inspired Approaches for Locomotion, Anomaly Detection and Reconfiguration for Walking Robots	Bojan Jakimovski	2011	Springer Berlin Heidelberg
Applied Cloud Deep Semantic Recognition: Advanced Anomaly Detection	Mehdi Roopaei; Paul Rad	2018	Routledge
A program to compute magnetic anomaly detection probabilities	R. N. Forrest	1990	Naval Postgraduate School;Available from National Technical Information Service
A program to compute electric anomaly detection probabilities	R. N. Forrest	1988	Naval Postgraduate School;Available from National Technical Information Service
New Developments in Unsupervised Outlier Detection: Algorithms and Applications	Xiaochun Wang; Xiali Wang; Mitch Wilkes	1st ed. 2021	Springer Nature Singapore
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video	Olga Isupova	1st ed. 2018	Springer International Publishing
Anomaly Detection as a Service: Challenges, Advances, and Opportunities	Danfeng (Daphne) Yao; Xiaokui Shu; Long Cheng; Salvatore J. Stolfo		Springer International Publishing
Supports ML-29-B, ML-29-D, ML-29-E; Anemometers ML-80, ML-80-A, ML-80-B; Indicators ML-117, ML-117-A, ML-117-B	United States. War Department	1944-09-12	U.S. G.P.O.
Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks	Muhammad Usman; Vallipuram Muthukkumarasamy; Xin-Wen Wu; Surraya Khanum	1st ed. 2018	Springer Nature Singapore
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Using unsupervised ML for anomaly detection		Machine Learning with the Elastic Stack: Gain valuable insights from your data with Elastic Stack's machine learning features	Rich Collier; Camilla Montonen; Bahaaldine Azarmi	2021	Packt Publishing
Unsupervised Anomaly Detection		Matlab. Statistics and Machine Learning Toolbox. User's Guide		2021	MathWorks
Anomaly detection with ML		Hands-On Industrial Internet of Things: Create a powerful Industrial IoT infrastructure using Industry 4. 0	Giacomo Veneri, Antonio Capasso	2018	Packt Publishing
Ensemble Algorithms for Unsupervised Anomaly Detection	Zhiruo Zhao; Kishan G. Mehrotra; Chilukuri K. Mohan	Current Approaches in Applied Artificial Intelligence: 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings	Moonis Ali; Young Sig Kwon; Chang-Hwan Lee; Juntae Kim; Yongdai Kim	2015	Springer International Publishing
Unsupervised Anomaly Detection Using HDG-Clustering Algorithm	Cheng-Fa Tsai; Chia-Chen Yen	Neural Information Processing: 14th International Confernce, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II	Masumi Ishikawa; Kenji Doya; Hiroyuki Miyamoto; Takeshi Yamakawa	2008	Springer Berlin Heidelberg
Anomaly Detection Using Unsupervised Machine Learning Algorithms	Pavitra Kadiyala; K. V. Shanmukhasai; Sai Shashank Budem; Praveen Kumar Reddy Maddikunta	Deep Learning for Security and Privacy Preservation in IoT	Aaisha Makkar; Neeraj Kumar	1st ed. 2021	Springer Nature Singapore
Unsupervised Anomaly Detection Using Optimal Transport for Predictive Maintenance	Amina Alaoui-Belghiti; Sylvain Chevallier; Eric Monacelli	Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV	Igor V. Tetko; Věra Kůrková; Pavel Karpov; Fabian Theis	1st ed. 2019	Springer International Publishing
The Anomaly Detection ML Task		Programming ML. NET (Developer Reference)	Dino Esposito, Francesco Esposito	2022	Microsoft Press
Improved Unsupervised Anomaly Detection Algorithm	Na Luo; Fuyu Yuan; Wanli Zuo; Fengling He; Zhiguo Zhou	Rough Sets and Knowledge Technology: Third International Conference, RSKT 2008, Chengdu, China, May 17-19, 2008, Proceedings	Guoyin Wang; Tianrui Li; Jerzy W. Grzymala-Busse; Duoqian Miao; Yiyu Y. Yao	2008	Springer Berlin Heidelberg
Unsupervised 3D Brain Anomaly Detection	Jaime Simarro Viana; Ezequiel de la Rosa; Thijs Vande Vyvere; David Robben; Diana M. Sima; CENTER-TBI Participants and Investigators	Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I	Alessandro Crimi; Spyridon Bakas	1st ed. 2021	Springer International Publishing
A Geometric Framework for Unsupervised Anomaly Detection	Eleazar Eskin; Andrew Arnold; Michael Prerau; Leonid Portnoy; Sal Stolfo	Applications of Data Mining in Computer Security	Daniel Barbará; Sushil Jajodia	2002	Springer US
Unsupervised Clustering Approach for Network Anomaly Detection	Iwan Syarif; Adam Prugel-Bennett; Gary Wills	Networked Digital Technologies: 4th International Conference, NDT 2012, Dubai, UAE, April 24-26, 2012. Proceedings, Part I	Rachid Benlamri	2012	Springer Berlin Heidelberg
Unsupervised Anomaly-Based Malware Detection Using Hardware Features	Adrian Tang; Simha Sethumadhavan; Salvatore J. Stolfo	Research in Attacks, Intrusions and Defenses: 17th International Symposium, RAID 2014, Gothenburg, Sweden, September 17-19, 2014, Proceedings	Angelos Stavrou; Herbert Bos; Georgios Portokalidis	2014	Springer International Publishing
Unsupervised ML		The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting	David Ping	2022	Packt Publishing
Unsupervised ML		Marketing in Customer Technology Environments: Prospective Customers and Magical Worlds	Devanathan Sudharshan	2020	Emerald Publishing
ML: Unsupervised	Matt Wiley; Joshua F. Wiley	Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization	Matt Wiley; Joshua F. Wiley	1st ed.	Apress
Using anomaly detection		Microsoft Power BI Data Analyst Certification Guide: A comprehensive guide to becoming a confident and certified Power BI professional	Orrin Edenfield, Edward Corcoran	2022	Packt Publishing
Cost-Effective Anomaly Detection for Blockchain Transactions Using Unsupervised Learning	M. Deepa; D. Akila	Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS 2021	Sheng-Lung Peng; Sun-Yuan Hsieh; Suseendran Gopalakrishnan; Balaganesh Duraisamy	1st ed. 2021	Springer Nature Singapore
Using Autoencoders for Anomaly Detection		Deep Learning: DL4J and Beyond	Adam Gibson; Josh Patterson	2015	O'Reilly Media
Unsupervised Anomaly Detection in the Wild	David Zimmerer; Daniel Paech; Carsten Lüth; Jens Petersen; Gregor Köhler; Klaus Maier-Hein	Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022	Klaus Maier-Hein; Thomas M. Deserno; Heinz Handels; Andreas Maier; Christoph Palm; Thomas Tolxdorff	1. Aufl. 2022	Springer Fachmedien Wiesbaden
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