BSSML17 - Anomaly Detection
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Anomaly detection is an unsupervised learning technique to find unusual instances without labels. It works by defining an anomaly score where higher scores indicate more unusual instances. Techniques include univariate analysis using standard deviation, isolation forests, and Benford's law. Applications include filtering data to improve models, detecting fraud, intrusions, and mistakes. Anomaly detection also helps determine when models need retraining due to changes in input data.
BSSML17 - Anomaly Detection
- 1. N O V E M B E R 2 9 , 2 0 1 7
- 2. BigML, Inc 2 Anomaly Detection Finding the Unusual Poul Petersen CIO, BigML, Inc
- 3. BigML, Inc 3Anomaly Detection What is Anomaly Detection? • An unsupervised learning technique • No labels necessary • Useful for finding unusual instances • Filtering, finding mistakes, 1-class classifiers • Finds instances that do not match • Customer: big or small spender for profile • Medical: healthy patient despite indicative diagnostics • Defines each unusual instance by an “anomaly score” • in BigML: 0＝normal, 1＝unusual, and 0.7 ≫ 0.6 ﹥0.5 • Standard deviation, distributions, etc
- 4. BigML, Inc 4Anomaly Detection Clusters date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51
- 5. BigML, Inc 5Anomaly Detection Clusters date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 similar
- 6. BigML, Inc 6Anomaly Detection Anomaly Detection date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51
- 7. BigML, Inc 7Anomaly Detection Anomaly Detection date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 anomaly • Amount $2,459 is higher than all other transactions • It is the only transaction • In zip 21350 • for the purchase class "tech"
- 8. BigML, Inc 8Anomaly Detection Use Cases • Unusual instance discovery - "exploration" • Intrusion Detection - "looking for unusual usage patterns" • Fraud - "looking for unusual behavior" • Identify Incorrect Data - "looking for mistakes" • Remove Outliers - "improve model quality" • Model Competence / Input Data Drift
- 9. BigML, Inc 9Anomaly Detection Removing Outliers • Models need to generalize • Outliers negatively impact generalization GOAL: Use anomaly detector to identify most anomalous points and then remove them before modeling. DATASET FILTERED DATASET ANOMALY DETECTOR CLEAN MODEL
- 10. BigML, Inc 10Anomaly Detection Diabetes Anomalies DIABETES SOURCE DIABETES DATASET TRAIN SET TEST SET ALL MODEL CLEAN DATASET FILTER ALL MODEL ALL EVALUATION CLEAN EVALUATION COMPARE EVALUATIONS ANAOMALY DETECTOR
- 11. BigML, Inc 11 Anomaly Demo #1
- 12. BigML, Inc 12Anomaly Detection Intrusion Detection GOAL: Identify unusual command line behavior per user and across all users that might indicate an intrusion. • Dataset of command line history for users • Data for each user consists of commands, flags, working directories, etc. • Assumption: Users typically issue the same flag patterns and work in certain directories Per User Per Dir All User All Dir
- 13. BigML, Inc 13Anomaly Detection Fraud • Dataset of credit card transactions • Additional user profile information GOAL: Cluster users by profile and use multiple anomaly scores to detect transactions that are anomalous on multiple levels. Card Level User Level Similar User Level
- 14. BigML, Inc 14Anomaly Detection Model Competence • After putting a model it into production, data that is being predicted can become statistically different than the training data. • Train an anomaly detector at the same time as the model. GOAL: For every prediction, compute an anomaly score. If the anomaly score is high, then the model may not be competent and should not be trusted. Prediction T T Confidence 86 % 84 % Anomaly Score 0,5367 0,7124 Competent? Y N At Prediction TimeAt Training Time DATASET MODEL ANOMALY DETECTOR
- 15. BigML, Inc 15Anomaly Detection Benford’s Law • In real-life numeric sets the small digits occur disproportionately often as leading significant digits. • Applications include: • accounting records • electricity bills • street addresses • stock prices • population numbers • death rates • lengths of rivers • Available in BigML API
- 16. BigML, Inc 16Anomaly Detection Univariate Approach • Single variable: heights, test scores, etc • Assume the value is distributed “normally” • Compute standard deviation • a measure of how “spread out” the numbers are • the square root of the variance (The average of the squared differences from the Mean.) • Depending on the number of instances, choose a “multiple” of standard deviations to indicate an anomaly. A multiple of 3 for 1000 instances removes ~ 3 outliers.
- 17. BigML, Inc 17Anomaly Detection Univariate Approach measurement frequency outliersoutliers • Available in BigML API
- 18. BigML, Inc 18Anomaly Detection Multivariate Matters
- 19. BigML, Inc 19Anomaly Detection Multivariate Matters
- 20. BigML, Inc 20Anomaly Detection Human Expert Most Unusual?
- 21. BigML, Inc 21Anomaly Detection Human Expert “Round”“Skinny” “Corners” “Skinny” but not “smooth” No “Corners” Not “Round” Key Insight The “most unusual” object is different in some way from every partition of the features. Most unusual
- 22. BigML, Inc 22Anomaly Detection Human Expert • Human used prior knowledge to select possible features that separated the objects. • “round”, “skinny”, “smooth”, “corners” • Items were then separated based on the chosen features • Each cluster was then examined to see which object fit the least well in its cluster and did not fit any other cluster
- 23. BigML, Inc 23Anomaly Detection Human Expert • Length/Width • greater than 1 => “skinny” • equal to 1 => “round” • less than 1 => invert • Number of Surfaces • distinct surfaces require “edges” which have corners • easier to count • Smooth - true or false Create features that capture these object differences
- 24. BigML, Inc 24Anomaly Detection Anomaly Features Object Length / Width Num Surfaces Smooth penny 1 3 TRUE dime 1 3 TRUE knob 1 4 TRUE eraser 2,75 6 TRUE box 1 6 TRUE block 1,6 6 TRUE screw 8 3 FALSE battery 5 3 TRUE key 4,25 3 FALSE bead 1 2 TRUE
- 25. BigML, Inc 25Anomaly Detection length/width > 5 smooth? box blockeraser knob penny/dime bead key battery screw num surfaces = 6 length/width =1 length/width < 2 Know that “splits” matter - don’t know the order TrueFalse TrueFalse TrueFalse FalseTrue TrueFalse Random Splits
- 26. BigML, Inc 26Anomaly Detection Isolation Forest Grow a random decision tree until each instance from a sample is in its own leaf “easy” to isolate “hard” to isolate Depth Now repeat the process several times and use average Depth to compute anomaly score: 0 (similar) -> 1 (dissimilar)
- 27. BigML, Inc 27Anomaly Detection Isolation Forest Scoring D = 3 D = 6 D = 2 S=0.45 Map avg depth to final score f1 f2 f3 i1 red cat ball i2 red cat ball i3 red cat box i4 blue dog pen For the instance, i2 Find the depth in each tree
- 28. BigML, Inc 28Anomaly Detection Model Competence • A low anomaly score means the loan is similar to the modeled loans. • A high anomaly score means you should not trust the model. Prediction T T Confidence 86 % 84 % Anomaly Score 0,5367 0,7124 Competent? Y N OPEN LOANS PREDICTION ANOMALY SCORE CLOSED LOAN MODEL CLOSED LOAN ANOMALY DETECTOR
- 29. BigML, Inc 29 Anomaly Demo #2
- 30. BigML, Inc 30Anomaly Detection 1-Class Classifier? • You place an advertisement in a local newspaper • You collect demographic information about all responders • Now you want to market in a new locality with direct letters • To optimize mailing costs, need to predict who will respond • But, can not distinguish not interested from didn’t see the ad • Train an anomaly detector on the 1-class data • Pick the households with the lowest scores for mailing: • If a household has a low anomaly score, then they are “similar” to enough of your positive responders and therefore may respond as well • If an individual has a high anomaly score, then they are dissimilar from all previous responders and therefore are less likely to respond.
- 31. BigML, Inc 31Anomaly Detection Summary • Anomaly detection is the process of finding unusual instances • Some techniques and how they work: • Univariate: standard deviation • Benford’s law • Isolation Forest • Applications • Filtering to improve models • Finding mistakes, fraud, and intruders • Knowing when to retrain a model (competence) • 1-class classifiers • In general… unsupervised learning techniques: • Require more finesse and interpretation • Are more commonly part of a multistep workflow