Beymani consists of set of Hadoop, Spark and Storm based tools for outlier and anamoly detection, which can be used for fraud detection, intrusion detection.
- Simple to use
- Input output in CSV format
- Metadata defined in simple JSON file
- Extremely configurable with tons of configuration knobs
The following blogs of mine are good source of details of beymani
- http://pkghosh.wordpress.com/2012/01/02/fraudsters-outliers-and-big-data-2/
- http://pkghosh.wordpress.com/2012/02/18/fraudsters-are-not-model-citizens/
- http://pkghosh.wordpress.com/2012/06/18/its-a-lonely-life-for-outliers/
- http://pkghosh.wordpress.com/2012/10/18/relative-density-and-outliers/
- http://pkghosh.wordpress.com/2013/10/21/real-time-fraud-detection-with-sequence-mining/
- https://pkghosh.wordpress.com/2018/09/18/contextual-outlier-detection-with-statistical-modeling-on-spark/
- https://pkghosh.wordpress.com/2018/10/15/learning-alarm-threshold-from-user-feedback-using-decision-tree-on-spark/
- Multi variate instance distribution model
- Multi variate sequence or multi gram distribution model
- Average instance Distance
- Relative instance Density
- Markov chain with sequence data
- Instance clustering
- Sequence clustering
Project's resource directory has various tutorial documents for the use cases described in the blogs.
For Hadoop 1
- mvn clean install
For Hadoop 2 (non yarn)
- git checkout nuovo
- mvn clean install
For Hadoop 2 (yarn)
- git checkout nuovo
- mvn clean install -P yarn
For Spark
- mvn clean install
- sbt publishLocal
- in ./spark sbt clean package
Please feel free to email me at [email protected]
Contributors are welcome. Please email me at [email protected]