- published: 30 Dec 2014
- views: 3479
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This talk will describe recent work by the NASA Data Sciences Group on
data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify.
Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory.
Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms.
Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn (https://www.linkedin.com/in/elizabethanichols) for more information.
- published: 14 Nov 2015
- views: 382
"Science of Anomaly Detection"
Video Talk (17:08)
Scott Purdy
Engineering Manager, Numenta
Numenta Workshop
October 17, 2014
Redwood City, CA
- published: 16 Dec 2014
- views: 4178
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context.
Author: Clarence Chio
More: http://www.phdays.com/program/tech/40866/
- published: 27 Jul 2015
- views: 1014
Data Science London Meetup - Anomaly Detection
There has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It offers something to interest data scientists of all stripes, from interactive REPL to distributed functional programming to implementations of standard machine learning techniques.
In fact, it promises big scalability improvements over MapReduce for iterative algorithms, like k-means clustering, which can be used to detect anomalous data in a huge data set, for example.
This session will walk through a complete example of anomaly detection using Apache Spark and it's MLlib subproject, as applied to the well-known network intrusion detection data set from KDD Cup '99. It will impart a taste of Scala (Spark's native language), Spark's core concepts like RDDs, and usage of MLlib for k-means clustering, in real-time on a Hadoop cluster. It will also introduce the concept of k-means clustering and how a data scientist would iteratively improve clustering in a session with Spark.
Sean is Director of Data Science at Cloudera, based in London. Before Cloudera, he founded Myrrix Ltd, a company commercializing large-scale real-time recommender systems on Apache Hadoop. He has been a primary committer and VP for Apache Mahout, and co-author of Mahout in Action. Previously, Sean was a senior engineer at Google. He holds an MBA from the London Business School and a BA in Computer Science from Harvard.
Sorry about the audio quality.
http://datasciencelondon.org
- published: 19 Jul 2014
- views: 6519
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to provide a foundation to automate anomaly detection in surveilance video, utilizing contemporary methods of parallel computing. The processing, detection, and decision algorithms were written in C and Python (leveraging the PyCUDA module) and run on a designated NVIDIA Jetson TK1.
- published: 05 Dec 2015
- views: 199
Shankar Vedaraman and Chris Colburn, Netflix
Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used ubiquitously at Netflix. For example, data ware- housing applications use traditional outlier detection methods (e.g. some number of standard deviations) that work for low cardinality dimensions that are normally distributed. Problem is, dimensions of interest at Netflix are neither normally distributed nor have low cardinality and the traditional methods tends to produce high numbers of false positives/negatives in such scenarios.
In this session, a case study at Netflix will be presented where a variant of the Singular Value Decomposition is used for anomaly detection in high cardinality dimensions. A payment processing application, with more than 50M customers, many external participants in addition to Netflix’s systems, and high fault tolerance requirements, will be discussed. The wide array of banks that customers use to pay for Netflix creates the high cardinality dimension scenario, and the complexity of the payment transaction calls for a different solution than the traditional methods mentioned above. Finally, the decoupled architecture in the cloud that provides a highly performing, scalable solution, will also be presented.
Speaker Bios
Shankar Vederaman leads the Payment Analytic Data science and Engineering team at Netflix. His team is responsible for providing analytical solutions for Payments, Fraud and Retail gift analytics. The solutions include data engineering, BI engineering and analytical story telling. Shankar is highly passionate about creating data products that utilize the power of data science for better business benefits.
Prior to Netflix, Shankar was to Rovi Corporation where he led a Business Intelligence team to provide analytical solutions for various business units. Shankar has a Masters in Computer Science and is currently pursuing a Masters in Predictive Analytics program from Northwestern university to master the science behind data science.
Chris Colburn is member of the Product Analytics team at Netflix. Chris works to provide his business partners with the data, and insights, that they need to make good business decisions. Sometimes this can be done with a simple SQL query, sometimes this means writing custom code for big data, and sometimes utilizing data science techniques. Ultimately Chris hopes to create reusable tools that will enable other engineers/analysts at Netflix to do their jobs better (with the belief that this will yield a superior product). Prior to Netflix, Chris was with Teradata-Aster, where he supported pre-sales and professional service engagement help customers resolve their business problems using data science. Chris has a PhD in Engineering in with five years of research experience and two years of predictive modeling consulting experience.
http://www.meetup.com/SF-Bay-ACM/events/218481652/
http://www.sfbayacm.org/event/anomaly-detection-payment-processing-netflix
- published: 17 Feb 2015
- views: 1523
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. The framework uses a combination of anomaly detection techniques to detect local and global anomalies. The system detects several types of local anomalies using different anomaly detection techniques over a sliding window. We used rigorous data experiments to perform data preprocessing and select hyperparameters. We have employed statistics-based techniques to detect local point anomalies and statistical detection theory (Page’s Test) to detect local contextual anomalies. Time-series models are employed to detect seasonality in the data and detect global anomalies.
In this talk, I will showcase results of the HawkEye system on real-data.
- published: 17 Jul 2015
- views: 790
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management, TAU
"Academic Perspectives on Cyber Security Challenges",
The Yuval Ne'eman Science, Technology & Security Workshop,
Tel Aviv University,
15.9.14
- published: 06 Oct 2014
- views: 1036
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon.
Source code referenced is at https://github.com/subutai/nupic.subutai/tree/master/run_anomaly
- published: 08 May 2014
- views: 2620
Anomaly detection, also referred to as outlier detection refers to detecting patterns in a given data set that do not conform to an established normal behavior. The patterns thus detected are called anomalies and often translate to critical and actionable information in several application domains. Anomalies are also referred to as outliers, change, deviation, surprise, aberrant, peculiarity, intrusion, etc.
In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.[citation needed]
This page contains text from Wikipedia, the Free Encyclopedia -
http://en.wikipedia.org/wiki/Anomaly_detection
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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53:03Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at GoogleNikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley. -
29:38Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory. Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms. Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn (https://www.linkedin.com/in/elizabethanichols) for more information. -
17:14Science of Anomaly Detection
Science of Anomaly DetectionScience of Anomaly Detection
"Science of Anomaly Detection" Video Talk (17:08) Scott Purdy Engineering Manager, Numenta Numenta Workshop October 17, 2014 Redwood City, CA -
49:38Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
Detecting Network Intrusions With Machine Learning Based Anomaly Detection TechniquesDetecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/ -
33:09Anomaly Detection with Apache Spark - Sean Owen
Anomaly Detection with Apache Spark - Sean OwenAnomaly Detection with Apache Spark - Sean Owen
Data Science London Meetup - Anomaly Detection There has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It offers something to interest data scientists of all stripes, from interactive REPL to distributed functional programming to implementations of standard machine learning techniques. In fact, it promises big scalability improvements over MapReduce for iterative algorithms, like k-means clustering, which can be used to detect anomalous data in a huge data set, for example. This session will walk through a complete example of anomaly detection using Apache Spark and it's MLlib subproject, as applied to the well-known network intrusion detection data set from KDD Cup '99. It will impart a taste of Scala (Spark's native language), Spark's core concepts like RDDs, and usage of MLlib for k-means clustering, in real-time on a Hadoop cluster. It will also introduce the concept of k-means clustering and how a data scientist would iteratively improve clustering in a session with Spark. Sean is Director of Data Science at Cloudera, based in London. Before Cloudera, he founded Myrrix Ltd, a company commercializing large-scale real-time recommender systems on Apache Hadoop. He has been a primary committer and VP for Apache Mahout, and co-author of Mahout in Action. Previously, Sean was a senior engineer at Google. He holds an MBA from the London Business School and a BA in Computer Science from Harvard. Sorry about the audio quality. http://datasciencelondon.org -
35:03Real Time Anomaly Detection with Spark ML an Akka
Real Time Anomaly Detection with Spark ML an AkkaReal Time Anomaly Detection with Spark ML an Akka
-
2:15Surveillance and Automated Anomaly Detection
Surveillance and Automated Anomaly DetectionSurveillance and Automated Anomaly Detection
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to provide a foundation to automate anomaly detection in surveilance video, utilizing contemporary methods of parallel computing. The processing, detection, and decision algorithms were written in C and Python (leveraging the PyCUDA module) and run on a designated NVIDIA Jetson TK1. -
60:14Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
Shankar Vedaraman and Chris Colburn, Netflix Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used ubiquitously at Netflix. For example, data ware- housing applications use traditional outlier detection methods (e.g. some number of standard deviations) that work for low cardinality dimensions that are normally distributed. Problem is, dimensions of interest at Netflix are neither normally distributed nor have low cardinality and the traditional methods tends to produce high numbers of false positives/negatives in such scenarios. In this session, a case study at Netflix will be presented where a variant of the Singular Value Decomposition is used for anomaly detection in high cardinality dimensions. A payment processing application, with more than 50M customers, many external participants in addition to Netflix’s systems, and high fault tolerance requirements, will be discussed. The wide array of banks that customers use to pay for Netflix creates the high cardinality dimension scenario, and the complexity of the payment transaction calls for a different solution than the traditional methods mentioned above. Finally, the decoupled architecture in the cloud that provides a highly performing, scalable solution, will also be presented. Speaker Bios Shankar Vederaman leads the Payment Analytic Data science and Engineering team at Netflix. His team is responsible for providing analytical solutions for Payments, Fraud and Retail gift analytics. The solutions include data engineering, BI engineering and analytical story telling. Shankar is highly passionate about creating data products that utilize the power of data science for better business benefits. Prior to Netflix, Shankar was to Rovi Corporation where he led a Business Intelligence team to provide analytical solutions for various business units. Shankar has a Masters in Computer Science and is currently pursuing a Masters in Predictive Analytics program from Northwestern university to master the science behind data science. Chris Colburn is member of the Product Analytics team at Netflix. Chris works to provide his business partners with the data, and insights, that they need to make good business decisions. Sometimes this can be done with a simple SQL query, sometimes this means writing custom code for big data, and sometimes utilizing data science techniques. Ultimately Chris hopes to create reusable tools that will enable other engineers/analysts at Netflix to do their jobs better (with the belief that this will yield a superior product). Prior to Netflix, Chris was with Teradata-Aster, where he supported pre-sales and professional service engagement help customers resolve their business problems using data science. Chris has a PhD in Engineering in with five years of research experience and two years of predictive modeling consulting experience. http://www.meetup.com/SF-Bay-ACM/events/218481652/ http://www.sfbayacm.org/event/anomaly-detection-payment-processing-netflix -
2:38Anomaly Detected (Royalty Free Music) [CC-BY]
Anomaly Detected (Royalty Free Music) [CC-BY] -
19:42Satnam Singh - HawkEye: A Real Time Anomaly Detection System
Satnam Singh - HawkEye: A Real Time Anomaly Detection SystemSatnam Singh - HawkEye: A Real Time Anomaly Detection System
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. The framework uses a combination of anomaly detection techniques to detect local and global anomalies. The system detects several types of local anomalies using different anomaly detection techniques over a sliding window. We used rigorous data experiments to perform data preprocessing and select hyperparameters. We have employed statistics-based techniques to detect local point anomalies and statistical detection theory (Page’s Test) to detect local contextual anomalies. Time-series models are employed to detect seasonality in the data and detect global anomalies. In this talk, I will showcase results of the HawkEye system on real-data. -
17:27Anomaly Detection by Machine Learning Tools
Anomaly Detection by Machine Learning ToolsAnomaly Detection by Machine Learning Tools
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management, TAU "Academic Perspectives on Cyber Security Challenges", The Yuval Ne'eman Science, Technology & Security Workshop, Tel Aviv University, 15.9.14 -
50:48Anomaly Detection in CLA
Anomaly Detection in CLAAnomaly Detection in CLA
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon. Source code referenced is at https://github.com/subutai/nupic.subutai/tree/master/run_anomaly
-
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his r... -
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory. Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and ... -
Science of Anomaly Detection
"Science of Anomaly Detection" Video Talk (17:08) Scott Purdy Engineering Manager, Numenta Numenta Workshop October 17, 2014 Redwood City, CA -
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/ -
Anomaly Detection with Apache Spark - Sean Owen
Data Science London Meetup - Anomaly Detection There has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It offers something to interest data scientists of all stripes, from interactive REPL to distributed functional programming to implementations of standard machine learning techniques. In fact, it promises big scalability improvements over MapReduce for iterative algorithms, like k-means clustering, which can be used to detect anomalous data in a huge data set, for example. This session will walk through a complete example of anomaly detection using Apache Spark and it's MLlib subproject, as applied to the well-known network intrusion detection data set from KDD Cup '99. It will impart a taste of Scala (Spark's native language), Spark... -
Real Time Anomaly Detection with Spark ML an Akka
-
Surveillance and Automated Anomaly Detection
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to provide a foundation to automate anomaly detection in surveilance video, utilizing contemporary methods of parallel computing. The processing, detection, and decision algorithms were written in C and Python (leveraging the PyCUDA module) and run on a designated NVIDIA Jetson TK1. -
Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
Shankar Vedaraman and Chris Colburn, Netflix Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used ubiquitously at Netflix. For example, data ware- housing applications use traditional outlier detection methods (e.g. some number of standard deviations) that work for low cardinality dimensions that are normally distributed. Problem is, dimensions of interest at Netflix are neither normally distributed nor have low cardinality and the traditional methods tends to produce high numbers of false positives/negatives in such scenarios. In this session, a case study at Netflix will be presented where a variant of the Singular Value Decomposition is used for anomaly detection in high cardinality dimensions. A payment processing applicat... -
-
Satnam Singh - HawkEye: A Real Time Anomaly Detection System
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. The framework uses a combination of anomaly detection techniques to detect local and global anomalies. The system detects several types of local anomalies using different anomaly detection techniques over a sliding window. We used rigorous data experiments to perform data preprocessing and select hyperparameters. We have employed statistics-based techniques to detect local point anomalies and statistical detection theory (Page’s Test) to detect local contextual anomalies. Time-series models are employed to detect seasonality in the data and detect global anomalies. In this talk, I will showcase results of the HawkEye system on real-d... -
Anomaly Detection by Machine Learning Tools
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management, TAU "Academic Perspectives on Cyber Security Challenges", The Yuval Ne'eman Science, Technology & Security Workshop, Tel Aviv University, 15.9.14 -
Anomaly Detection in CLA
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon. Source code referenced is at https://github.com/subutai/nupic.subutai/tree/master/run_anomaly
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
- Order: Reorder
- Duration: 53:03
- Updated: 30 Dec 2014
- views: 3479
This talk will describe recent work by the NASA Data Sciences Group on
data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, an...
This talk will describe recent work by the NASA Data Sciences Group on
data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify.
Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
wn.com/Nikunj Oza Data Driven Anomaly Detection | Talks At Google
This talk will describe recent work by the NASA Data Sciences Group on
data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify.
Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
- published: 30 Dec 2014
- views: 3479
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
- Order: Reorder
- Duration: 29:38
- Updated: 14 Nov 2015
- views: 382
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that a...
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory.
Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms.
Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn (https://www.linkedin.com/in/elizabethanichols) for more information.
wn.com/Anomaly Detection 101 Elizabeth (Betsy) Nichols Ph.D.
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory.
Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms.
Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn (https://www.linkedin.com/in/elizabethanichols) for more information.
- published: 14 Nov 2015
- views: 382
Science of Anomaly Detection
- Order: Reorder
- Duration: 17:14
- Updated: 16 Dec 2014
- views: 4178
"Science of Anomaly Detection"
Video Talk (17:08)
Scott Purdy
Engineering Manager, Numenta
Numenta Workshop
October 17, 2014
Redwood City, CA
"Science of Anomaly Detection"
Video Talk (17:08)
Scott Purdy
Engineering Manager, Numenta
Numenta Workshop
October 17, 2014
Redwood City, CA
wn.com/Science Of Anomaly Detection
"Science of Anomaly Detection"
Video Talk (17:08)
Scott Purdy
Engineering Manager, Numenta
Numenta Workshop
October 17, 2014
Redwood City, CA
- published: 16 Dec 2014
- views: 4178
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
- Order: Reorder
- Duration: 49:38
- Updated: 27 Jul 2015
- views: 1014
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze...
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context.
Author: Clarence Chio
More: http://www.phdays.com/program/tech/40866/
wn.com/Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context.
Author: Clarence Chio
More: http://www.phdays.com/program/tech/40866/
- published: 27 Jul 2015
- views: 1014
Anomaly Detection with Apache Spark - Sean Owen
- Order: Reorder
- Duration: 33:09
- Updated: 19 Jul 2014
- views: 6519
Data Science London Meetup - Anomaly Detection
There has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It ...
Data Science London Meetup - Anomaly Detection
There has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It offers something to interest data scientists of all stripes, from interactive REPL to distributed functional programming to implementations of standard machine learning techniques.
In fact, it promises big scalability improvements over MapReduce for iterative algorithms, like k-means clustering, which can be used to detect anomalous data in a huge data set, for example.
This session will walk through a complete example of anomaly detection using Apache Spark and it's MLlib subproject, as applied to the well-known network intrusion detection data set from KDD Cup '99. It will impart a taste of Scala (Spark's native language), Spark's core concepts like RDDs, and usage of MLlib for k-means clustering, in real-time on a Hadoop cluster. It will also introduce the concept of k-means clustering and how a data scientist would iteratively improve clustering in a session with Spark.
Sean is Director of Data Science at Cloudera, based in London. Before Cloudera, he founded Myrrix Ltd, a company commercializing large-scale real-time recommender systems on Apache Hadoop. He has been a primary committer and VP for Apache Mahout, and co-author of Mahout in Action. Previously, Sean was a senior engineer at Google. He holds an MBA from the London Business School and a BA in Computer Science from Harvard.
Sorry about the audio quality.
http://datasciencelondon.org
wn.com/Anomaly Detection With Apache Spark Sean Owen
Data Science London Meetup - Anomaly Detection
There has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It offers something to interest data scientists of all stripes, from interactive REPL to distributed functional programming to implementations of standard machine learning techniques.
In fact, it promises big scalability improvements over MapReduce for iterative algorithms, like k-means clustering, which can be used to detect anomalous data in a huge data set, for example.
This session will walk through a complete example of anomaly detection using Apache Spark and it's MLlib subproject, as applied to the well-known network intrusion detection data set from KDD Cup '99. It will impart a taste of Scala (Spark's native language), Spark's core concepts like RDDs, and usage of MLlib for k-means clustering, in real-time on a Hadoop cluster. It will also introduce the concept of k-means clustering and how a data scientist would iteratively improve clustering in a session with Spark.
Sean is Director of Data Science at Cloudera, based in London. Before Cloudera, he founded Myrrix Ltd, a company commercializing large-scale real-time recommender systems on Apache Hadoop. He has been a primary committer and VP for Apache Mahout, and co-author of Mahout in Action. Previously, Sean was a senior engineer at Google. He holds an MBA from the London Business School and a BA in Computer Science from Harvard.
Sorry about the audio quality.
http://datasciencelondon.org
- published: 19 Jul 2014
- views: 6519
Real Time Anomaly Detection with Spark ML an Akka
- Order: Reorder
- Duration: 35:03
- Updated: 28 Oct 2015
- views: 2242
- published: 28 Oct 2015
- views: 2242
Surveillance and Automated Anomaly Detection
- Order: Reorder
- Duration: 2:15
- Updated: 05 Dec 2015
- views: 199
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to provide a foundation to automate anomaly detection in surveilance vide...
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to provide a foundation to automate anomaly detection in surveilance video, utilizing contemporary methods of parallel computing. The processing, detection, and decision algorithms were written in C and Python (leveraging the PyCUDA module) and run on a designated NVIDIA Jetson TK1.
wn.com/Surveillance And Automated Anomaly Detection
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to provide a foundation to automate anomaly detection in surveilance video, utilizing contemporary methods of parallel computing. The processing, detection, and decision algorithms were written in C and Python (leveraging the PyCUDA module) and run on a designated NVIDIA Jetson TK1.
- published: 05 Dec 2015
- views: 199
Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
- Order: Reorder
- Duration: 60:14
- Updated: 17 Feb 2015
- views: 1523
Shankar Vedaraman and Chris Colburn, Netflix
Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used...
Shankar Vedaraman and Chris Colburn, Netflix
Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used ubiquitously at Netflix. For example, data ware- housing applications use traditional outlier detection methods (e.g. some number of standard deviations) that work for low cardinality dimensions that are normally distributed. Problem is, dimensions of interest at Netflix are neither normally distributed nor have low cardinality and the traditional methods tends to produce high numbers of false positives/negatives in such scenarios.
In this session, a case study at Netflix will be presented where a variant of the Singular Value Decomposition is used for anomaly detection in high cardinality dimensions. A payment processing application, with more than 50M customers, many external participants in addition to Netflix’s systems, and high fault tolerance requirements, will be discussed. The wide array of banks that customers use to pay for Netflix creates the high cardinality dimension scenario, and the complexity of the payment transaction calls for a different solution than the traditional methods mentioned above. Finally, the decoupled architecture in the cloud that provides a highly performing, scalable solution, will also be presented.
Speaker Bios
Shankar Vederaman leads the Payment Analytic Data science and Engineering team at Netflix. His team is responsible for providing analytical solutions for Payments, Fraud and Retail gift analytics. The solutions include data engineering, BI engineering and analytical story telling. Shankar is highly passionate about creating data products that utilize the power of data science for better business benefits.
Prior to Netflix, Shankar was to Rovi Corporation where he led a Business Intelligence team to provide analytical solutions for various business units. Shankar has a Masters in Computer Science and is currently pursuing a Masters in Predictive Analytics program from Northwestern university to master the science behind data science.
Chris Colburn is member of the Product Analytics team at Netflix. Chris works to provide his business partners with the data, and insights, that they need to make good business decisions. Sometimes this can be done with a simple SQL query, sometimes this means writing custom code for big data, and sometimes utilizing data science techniques. Ultimately Chris hopes to create reusable tools that will enable other engineers/analysts at Netflix to do their jobs better (with the belief that this will yield a superior product). Prior to Netflix, Chris was with Teradata-Aster, where he supported pre-sales and professional service engagement help customers resolve their business problems using data science. Chris has a PhD in Engineering in with five years of research experience and two years of predictive modeling consulting experience.
http://www.meetup.com/SF-Bay-ACM/events/218481652/
http://www.sfbayacm.org/event/anomaly-detection-payment-processing-netflix
wn.com/Anomaly Detection For Payment Processing At Netflix, Shankar Vedaraman Chris Colburn 20150126
Shankar Vedaraman and Chris Colburn, Netflix
Anomaly detection is the process of identifying data points that do not conform to normal behavior, and it is used ubiquitously at Netflix. For example, data ware- housing applications use traditional outlier detection methods (e.g. some number of standard deviations) that work for low cardinality dimensions that are normally distributed. Problem is, dimensions of interest at Netflix are neither normally distributed nor have low cardinality and the traditional methods tends to produce high numbers of false positives/negatives in such scenarios.
In this session, a case study at Netflix will be presented where a variant of the Singular Value Decomposition is used for anomaly detection in high cardinality dimensions. A payment processing application, with more than 50M customers, many external participants in addition to Netflix’s systems, and high fault tolerance requirements, will be discussed. The wide array of banks that customers use to pay for Netflix creates the high cardinality dimension scenario, and the complexity of the payment transaction calls for a different solution than the traditional methods mentioned above. Finally, the decoupled architecture in the cloud that provides a highly performing, scalable solution, will also be presented.
Speaker Bios
Shankar Vederaman leads the Payment Analytic Data science and Engineering team at Netflix. His team is responsible for providing analytical solutions for Payments, Fraud and Retail gift analytics. The solutions include data engineering, BI engineering and analytical story telling. Shankar is highly passionate about creating data products that utilize the power of data science for better business benefits.
Prior to Netflix, Shankar was to Rovi Corporation where he led a Business Intelligence team to provide analytical solutions for various business units. Shankar has a Masters in Computer Science and is currently pursuing a Masters in Predictive Analytics program from Northwestern university to master the science behind data science.
Chris Colburn is member of the Product Analytics team at Netflix. Chris works to provide his business partners with the data, and insights, that they need to make good business decisions. Sometimes this can be done with a simple SQL query, sometimes this means writing custom code for big data, and sometimes utilizing data science techniques. Ultimately Chris hopes to create reusable tools that will enable other engineers/analysts at Netflix to do their jobs better (with the belief that this will yield a superior product). Prior to Netflix, Chris was with Teradata-Aster, where he supported pre-sales and professional service engagement help customers resolve their business problems using data science. Chris has a PhD in Engineering in with five years of research experience and two years of predictive modeling consulting experience.
http://www.meetup.com/SF-Bay-ACM/events/218481652/
http://www.sfbayacm.org/event/anomaly-detection-payment-processing-netflix
- published: 17 Feb 2015
- views: 1523
Anomaly Detected (Royalty Free Music) [CC-BY]
- Order: Reorder
- Duration: 2:38
- Updated: 24 Oct 2011
- views: 81376
Download mp3 at: https://machinimasound.com/music/anomaly-detected
License: https://machinimasound.com/license
Download mp3 at: https://machinimasound.com/music/anomaly-detected
License: https://machinimasound.com/license
wn.com/Anomaly Detected (Royalty Free Music) Cc By
Satnam Singh - HawkEye: A Real Time Anomaly Detection System
- Order: Reorder
- Duration: 19:42
- Updated: 17 Jul 2015
- views: 790
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. T...
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. The framework uses a combination of anomaly detection techniques to detect local and global anomalies. The system detects several types of local anomalies using different anomaly detection techniques over a sliding window. We used rigorous data experiments to perform data preprocessing and select hyperparameters. We have employed statistics-based techniques to detect local point anomalies and statistical detection theory (Page’s Test) to detect local contextual anomalies. Time-series models are employed to detect seasonality in the data and detect global anomalies.
In this talk, I will showcase results of the HawkEye system on real-data.
wn.com/Satnam Singh Hawkeye A Real Time Anomaly Detection System
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. The framework uses a combination of anomaly detection techniques to detect local and global anomalies. The system detects several types of local anomalies using different anomaly detection techniques over a sliding window. We used rigorous data experiments to perform data preprocessing and select hyperparameters. We have employed statistics-based techniques to detect local point anomalies and statistical detection theory (Page’s Test) to detect local contextual anomalies. Time-series models are employed to detect seasonality in the data and detect global anomalies.
In this talk, I will showcase results of the HawkEye system on real-data.
- published: 17 Jul 2015
- views: 790
Anomaly Detection by Machine Learning Tools
- Order: Reorder
- Duration: 17:27
- Updated: 06 Oct 2014
- views: 1036
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management, TAU
"Academic Perspectives on Cyber Security Challenges",
The Yuval ...
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management, TAU
"Academic Perspectives on Cyber Security Challenges",
The Yuval Ne'eman Science, Technology & Security Workshop,
Tel Aviv University,
15.9.14
wn.com/Anomaly Detection By Machine Learning Tools
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management, TAU
"Academic Perspectives on Cyber Security Challenges",
The Yuval Ne'eman Science, Technology & Security Workshop,
Tel Aviv University,
15.9.14
- published: 06 Oct 2014
- views: 1036
Anomaly Detection in CLA
- Order: Reorder
- Duration: 50:48
- Updated: 08 May 2014
- views: 2620
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon.
Source code referenced is at https://github.com/subutai/nupic.subutai/tree/master/run_anomaly
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon.
Source code referenced is at https://github.com/subutai/nupic.subutai/tree/master/run_anomaly
wn.com/Anomaly Detection In Cla
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon.
Source code referenced is at https://github.com/subutai/nupic.subutai/tree/master/run_anomaly
- published: 08 May 2014
- views: 2620
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53:03
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
This talk will describe recent work by the NASA Data Sciences Group on
data-driven anomaly...
published: 30 Dec 2014
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
- Report rights infringement
- published: 30 Dec 2014
- views: 3479
29:38
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
This presentation surveys a collection of techniques for detecting anomalies in a DevOps e...
published: 14 Nov 2015
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
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- published: 14 Nov 2015
- views: 382
17:14
Science of Anomaly Detection
"Science of Anomaly Detection"
Video Talk (17:08)
Scott Purdy
Engineering Manager, Nument...
published: 16 Dec 2014
Science of Anomaly Detection
Science of Anomaly Detection
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- published: 16 Dec 2014
- views: 4178
49:38
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
Machine learning techniques used in network intrusion detection are susceptible to “model ...
published: 27 Jul 2015
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
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- published: 27 Jul 2015
- views: 1014
33:09
Anomaly Detection with Apache Spark - Sean Owen
Data Science London Meetup - Anomaly Detection
There has been an explosion of interest in...
published: 19 Jul 2014
Anomaly Detection with Apache Spark - Sean Owen
Anomaly Detection with Apache Spark - Sean Owen
- Report rights infringement
- published: 19 Jul 2014
- views: 6519
35:03
Real Time Anomaly Detection with Spark ML an Akka
published: 28 Oct 2015
Real Time Anomaly Detection with Spark ML an Akka
Real Time Anomaly Detection with Spark ML an Akka
- Report rights infringement
- published: 28 Oct 2015
- views: 2242
2:15
Surveillance and Automated Anomaly Detection
This project, completed for the Fall 2015 CS205 course at Harvard University, attempts to ...
published: 05 Dec 2015
Surveillance and Automated Anomaly Detection
Surveillance and Automated Anomaly Detection
- Report rights infringement
- published: 05 Dec 2015
- views: 199
60:14
Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
Shankar Vedaraman and Chris Colburn, Netflix
Anomaly detection is the process of identify...
published: 17 Feb 2015
Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
Anomaly Detection for Payment Processing at Netflix, Shankar Vedaraman & Chris Colburn 20150126
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- published: 17 Feb 2015
- views: 1523
2:38
Anomaly Detected (Royalty Free Music) [CC-BY]
Download mp3 at: https://machinimasound.com/music/anomaly-detected
License: https://machin...
published: 24 Oct 2011
Anomaly Detected (Royalty Free Music) [CC-BY]
Anomaly Detected (Royalty Free Music) [CC-BY]
- Report rights infringement
- published: 24 Oct 2011
- views: 81376
19:42
Satnam Singh - HawkEye: A Real Time Anomaly Detection System
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastru...
published: 17 Jul 2015
Satnam Singh - HawkEye: A Real Time Anomaly Detection System
Satnam Singh - HawkEye: A Real Time Anomaly Detection System
- Report rights infringement
- published: 17 Jul 2015
- views: 790
17:27
Anomaly Detection by Machine Learning Tools
Speaker: Prof. Irad Ben-Gal, Head of the Department of Industrial Engineering & Management...
published: 06 Oct 2014
Anomaly Detection by Machine Learning Tools
Anomaly Detection by Machine Learning Tools
- Report rights infringement
- published: 06 Oct 2014
- views: 1036
50:48
Anomaly Detection in CLA
By Subutai Ahmad, presented at 2014 Spring NuPIC Hackathon.
Source code referenced is at ...
published: 08 May 2014
Anomaly Detection in CLA
Anomaly Detection in CLA
- Report rights infringement
- published: 08 May 2014
- views: 2620
Nikunj Oza: "Data-driven Anomaly Detection" | Talk...
Anomaly Detection 101 - Elizabeth (Betsy) Nichols ...
Science of Anomaly Detection...
Detecting Network Intrusions With Machine Learning...
Anomaly Detection with Apache Spark - Sean Owen...
Real Time Anomaly Detection with Spark ML an Akka...
Surveillance and Automated Anomaly Detection...
Anomaly Detection for Payment Processing at Netfli...
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Anomaly Detected (Royalty Free Music) [CC-BY]...
Satnam Singh - HawkEye: A Real Time Anomaly Detect...
Anomaly Detection by Machine Learning Tools...
Anomaly Detection in CLA...
photo: AP / Department of National Parks, Wildlife and Plant Conservation
Dozens of dead tiger cubs found in freezer at Thai temple
Edit The Irish Times 01 Jun 2016
Thai wildlife authorities found 40 tiger cub carcasses in a freezer in Thailand’s infamous Tiger Temple on Wednesday as they removed live animals in response to international pressure over suspected trafficking and abuse ... But the temple has been investigated for suspected links to wildlife trafficking and abuse ... Reuters. . . ....
photo: Via YouTube
Baby with Zika-related microcephaly born at New Jersey hospital
Edit CNN 01 Jun 2016
(CNN)A baby with Zika virus-related microcephaly was born in New Jersey on Tuesday, hospital officials said. The baby and mother are stable and doing well following the cesarean delivery, said Dr. Abdulla Al-Kahan, director of maternal and fetal medicine at Hackensack University Medical Center. "The mother is stable, obviously sad, which is the normal emotional reaction given the situation," he said ... Read More ... CDC confirmation ... ....
photo: Creative Commons / Ted Eytan
Dems show true colors, want Clinton to run even if indicted
Edit The Examiner 31 May 2016
In a stunning revelation, 71 percent of likely voters who identified themselves as Democrats told the Rasmussen survey, published today, that they believe Hillary Rodham Clinton should continue running for the presidency if she is indicted in connection with the use of a private e-mail server while Secretary of State. Today’s Seattle Times fact-checks some of her statements regarding the e-mail scandal ... ....
photo: Creative Commons / Wdwdbot
World's longest and deepest rail tunnel to open in Switzerland
Edit BBC News 01 Jun 2016
The world's longest and deepest rail tunnel is to be officially opened in Switzerland, after almost two decades of construction work. The 57km (35-mile) twin-bore Gotthard tunnel will provide a high-speed rail link under the Swiss Alps between northern and southern Europe. Switzerland says it will revolutionise European freight transport. Goods currently carried on the route by a million lorries a year will go by train instead ... ....
photo: AP / U.S. Customs and Border Protection via AP
U.S. Government Seeks Seizure Of Insurance Payments To San Bernardino Shooter's Family
Edit WorldNews.com 01 Jun 2016
U.S. Atty ... “We will continue to use every tool available to seek justice on behalf of the victims of the San Bernardino terrorist attacks.”Farook and his wife, Tashfeen Malik, opened fire on San Bernardino County workers at a holiday party on Dec ... ....
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Featurespace closes £6.2 million funding round (Imperial Innovations Group plc)
Edit Public Technologies 31 May 2016
Featurespace is an Adaptive Behavioural Analytics company which has the ability to develop an understanding of normal patterns of behaviour and anomaly detection for individuals in real time ... They are targeting a growing global fraud detection market which was worth $2.4bn in 2014 and is growing at 30% per annum, according to Forrester....
MarketsandMarkets Selects Grid4C as a Key Innovator in the Internet of Things (IoT) for Utility Market
Edit Yahoo Daily News 31 May 2016
AUSTIN, Texas--(BUSINESS WIRE)-- ... This Smart News Release features multimedia. View the full release here ... Dr ... Its portfolio consists of a Load Research and Forecasting solution, providing accurate forecasts at the meter/sub-meter and sub-hour levels, Customer Engagement solutions which include non-intrusive household anomaly detection and load disaggregation capabilities, Demand Response Optimization, Customer Segmentation, and more ... ....
NexDefense, General Dynamics Commercial Cyber Services ink deal (ATDC - Advanced Technology Development Center)
Edit Public Technologies 28 May 2016
Under the terms of the agreement, General Dynamics Commercial Cyber Services will expand its portfolio of cybersecurity services to include monitoring of ICS networks utilizing NexDefense's Sophia™, an award winning industrial network security monitoring and anomaly detection system ... Sophia™, its patent-pending Industrial Network Anomaly Detection ......
25 May 2016 Killaloe Joint Venture (Cullen Resources Limited)
Edit Public Technologies 25 May 2016
The planned IP survey will be carried out in E63/1018 (MAT 80%, CUL 20%) and will cover 5 targets (Windy Hill, Cashel, Shinboner North, Shinboner South and Duke) where anomalous gold has been previously intersected in shallow drill holes which test extensive soil gold anomalies ... IP anomalies detected will potentially be more robust drill targets for gold, than targets based solely on soil gold anomalism....
IP Survey to Commence Killaloe Project (Matsa Resources Limited)
Edit Public Technologies 25 May 2016
The planned IP survey will be carried out in E63/1018 (MAT 80%, CUL 20%) and will cover 5 targets (Windy Hill, Cashel, Shinboner North, Shinboner South and Duke) where anomalous gold has been previously intersected in shallow drill holes which test extensive soil gold anomalies ... IP anomalies detected will potentially be more robust drill targets for gold, than targets based solely on soil gold anomalism....
Bloomberg and “The Magic” of Machine Learning (Bloomberg LP)
Edit Public Technologies 25 May 2016
(Source. Bloomberg LP). Bloomberg and 'The Magic' of Machine Learning. Gary Kazantsev is a researcher and software engineer who leads machine learning at Bloomberg. Machine learning is an increasingly important area at Bloomberg, a company that manages massive amounts of data in a real-time environment ... The conversation with Gary has been edited for length ... anomaly detection, text topic clustering, classification, and novelty detection....
GE Signs Digital Power Plant Agreements with Pakistan’s Halmore and Orient Power to Maximize System Efficiency (GE - General Electric Company)
Edit Public Technologies 25 May 2016
(Source. GE - General Electric Company). Islamabad, Pakistan; May 25, 2016. GE (NYSE. GE) has signed contracts with Halmore Power Generation Company Limited and Orient Power Company Ltd to provide its Predix-enabled Digital Power Plant solutions ... APM's anomaly detection capability helps predict outages before they happen, which enables just-in-time maintenance optimization and reduced plant downtime ... Note to Editors. About GE.. GE (NYSE....
GE Renewable Energy Introduces New Suite of Digital Wind Farm Apps (GE - General Electric Company)
Edit Public Technologies 24 May 2016
(Source. GE - General Electric Company). NEW ORLEANS-May 24, 2016-Today at the American Wind Energy Association's WINDPOWER 2016 event, GE Renewable Energy (NYSE. GE) introduced a new software applications suite for its Digital Wind Farm ecosystem ... The Diagnostics app uses operating data for advanced anomaly detection analysis and then incorporates that analysis into a detailed case management and recommendation system ... About GE. GE (NYSE....
GE Renewable Energy Introduces New Suite of Digital Wind Farm Apps
Edit Stockhouse 24 May 2016
Today at the American Wind Energy Association’s WINDPOWER 2016 event, GE Renewable Energy (NYSE. GE) introduced a new software applications suite for its Digital Wind Farm ecosystem ... That’s the benefit of the Digital Wind Farm—it’s always learning.” ... The Diagnostics app uses operating data for advanced anomaly detection analysis and then incorporates that analysis into a detailed case management and recommendation system ... About GE....
Gottfried Leibbrandt on cyber security and innovation (SWIFT - Society for Worldwide Interbank Financial Telecommunication SCRL)
Edit Public Technologies 24 May 2016
... develop indicators of compromise to help those banks improve their detective capabilities ... You all know what I'm talking about - bring on the next generation of pattern recognition, monitoring, anomaly detection, authentication, biometrics-and a host of innovations we have yet to develop that will improve and preserve the security of our industry....
IBM Enables Brands to Deliver the Next Wave of Consumer Experiences (IBM Danmark A/S)
Edit Public Technologies 21 May 2016
(Source. IBM Danmark A/S). ARMONK, NY and TAMPA, FL - 17 May 2016. IBM (NYSE ... Commerce Insights is also infusing cognitive into its anomaly detection capabilities so teams are automatically alerted to significant spikes and dips in sales and presented contributing factors such as inventory, promotional event, channel activities and soon input from social sentiment and competitive pricing ... Driving Continued Client Innovation ... (noodl....
General Dynamics Commercial Cyber Services Expands Cyber Monitoring Capabilities to Industrial Control Systems (General Dynamics Information Technology Inc)
Edit Public Technologies 20 May 2016
(Source ... HERNDON, Va ... General Dynamics Commercial Cyber Services will offer cyber monitoring for industrial controls, with the use of an Industrial Network Anomaly Detection (INAD) system within security operations centers (SOCs). When correlated with security event data the INAD allows cyber analysts to detect deviations from industrial control systems, alerting the customer of a potential cyber-attack on their network ... (noodl....
IDC Innovators for the 2016 Cloud-enabled Mobile Security Products Announced (IDC Research Inc)
Edit Public Technologies 19 May 2016
Cloud-connectivity - either as a delivery mechanism for detection capabilities, or as a back-end ... Ziperium provides a suite of mobile endpoint protection capabilities, but its flagship zIPS, an on-device mobile intrusion prevention system, separates it from the pack of mobile cloud security providers as it relies solely on behavior anomaly detection....
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