https://github.com/sec51/goanomaly

Golang library for anomaly detection. Uses the Gaussian distribution and the probability density formula.
https://github.com/sec51/goanomaly

Last synced: 11 days ago
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Golang library for anomaly detection. Uses the Gaussian distribution and the probability density formula.

• Host: GitHub
• URL: https://github.com/sec51/goanomaly
• Owner: sec51
• License: isc
• Created: 2016-01-04T19:20:52.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2017-05-22T08:18:02.000Z (over 7 years ago)
• Last Synced: 2024-08-01T04:02:18.160Z (3 months ago)
• Language: Go
• Size: 8.79 KB
• Stars: 17
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-golang-ai - goanomaly

README

        
[![Build Status](https://travis-ci.org/sec51/goanomaly.svg)](https://travis-ci.org/sec51/goanomaly) [![GoDoc](https://godoc.org/github.com/golang/gddo?status.svg)](https://godoc.org/github.com/sec51/goanomaly/)

### Anomaly detection library in golang, implemented via Gaussian distribution

1. Choose feature `x(i)` that might be indicative of anomalous examples

2. Estimate parameters by calculating the `mean` and `standard deviation`

3. Given a new example `x`, compute `p(x)` via the Gaussian normal distribution formaula

4. Anomaly if `p(x) < k`

### Example

````

import (

	"goanomaly"

	"log"

	"math/big"

)

// ==============================================================

// get a set of data

dataSet := fakeFixedData()

// init the AnomalyDetection object

anomalyDetection := goanomaly.NewAnomalyDetection(dataSet...)

// Call EventIsAnomalous with a new data point, which you can test against, and a threshold 

anomaly, result := anomalyDetection.EventIsAnomalous(*big.NewFloat(5050), big.NewFloat(0.001))

if anomaly {

	log.Println("Data point", 5050, "is anomalous")

}

// ==============================================================

// helper function to fake fixed data

func fakeFixedData() []big.Float {

	var dataSet []big.Float

	baseInt := big.NewFloat(5000)

	

	// fake sample data

	var increment big.Float

	for i := 0; i < 999; i++ {

		baseInt = big.NewFloat(5000)

		if i > 200 && i < 800 {

			increment = *baseInt.Add(baseInt, deviationMaxFloat)

			dataSet = append(dataSet, increment)		

		} else {

			increment = *baseInt.Add(baseInt, higherDeviationFloat)

			dataSet = append(dataSet, increment)			

		}

	}

	return dataSet

}

````

### Important

The Gaussian distribution anomaly detection usually does not keep into consideration the relation between the features.

For instance if you want to have a relation between say CPU load(x1) and Memory usage(x2) then you should create a 3rd feature in this way: (x1)/(x2)

and add it to the dataset

===

### LICENSE

Copyright (c) 2015-2016 Sec51.com 

Permission to use, copy, modify, and distribute this software for any

purpose with or without fee is hereby granted, provided that the above

copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES

WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF

MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR

ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES

WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN

ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF

OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.