From fraudulence to adversarial learning จรัล งามวิโรจน์เจริญ...
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From fraudulence to adversarial learning The First NIDA Business Analytics and Data Sciences Contest/Conference วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์ https://businessanalyticsnida.wordpress.com https://www.facebook.com/BusinessAnalyticsNIDA/ -- Fraudulent detection (ID Theft) approach & process - Evolution of fraudulence to sophisticated actor - adversarial learning จรัล งามวิโรจน์เจริญ Current chief data scientist and VP of Data Innovation Lab at Sertis, Former lead data scientist of Booz Allen Hamilton นวมินทราธิราช 3002 วันที่ 1 กันยายน 2559 15.15-15.45 น.
Transcript of From fraudulence to adversarial learning จรัล งามวิโรจน์เจริญ...
From fraudulence to adversarial learning
The First NIDA Business Analytics and Data Sciences Contest/Conferenceวันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์
https://businessanalyticsnida.wordpress.comhttps://www.facebook.com/BusinessAnalyticsNIDA/
-- Fraudulent detection (ID Theft) approach & process- Evolution of fraudulence to sophisticated actor - adversarial learning
จรัล งามวิโรจน์เจริญCurrent chief data scientist and VP of Data Innovation Lab at Sertis,Former lead data scientist of Booz Allen Hamilton
นวมินทราธิราช 3002 วันที่ 1 กันยายน 2559 15.15-15.45 น.
F r o m F r a u d u l e n c e t o a d v e r s a r i a l l e a r n i n g
Theft
Address
National IDPhone Number
Child NameSpouse Name
Bank Account
Credit Card Number
User Profile
Electronic Record
Who?ID Theft Definition
Business Objectives
• Financial/Medical/Insurance ID Theft
• Synthetic
• Account take over (ATO)
Common Type of ID Theft
Business
Objectives
Data
Exploration/
Preparation
DeploymentModeling Evaluation
Fraud Definition
Objectives
Account
Transaction
Behavior
External Data
Feature Engineering
Supervised Learning
Unsupervised Learning
Ensemble Model
Performance Metrics
Parameter Tuning
Platform Testing
Train vs Test
Fraud Modeling
Random Forest Support Vector Machine (SVM)
Deep Learning – Stacked denoising Autoencoder (SdA)U
nsup
erv
ised
Superv
ised
Multistage Ensemble Model
Feature
Extraction
Boosting
Feature Extraction - Ensemble
IDT NonIDT
Selected 8 2 10
Not Selected 8 982 990
16 984 1,000
Determined By Model’s Performance
IDT NonIDT
Selected 8 2 10
Not Selected 8 982 990
16 984 1,000
IDT Definition IDT Prevalence Estimate in Population
IDT NonIDT
Selected 8 2 10
Not Selected 8 982 990
16 984 1,000
Unverifiable
During the Operation
http://manager.co.th/Daily/ViewNews.aspx?NewsID=9590000083749
Dark Web Marketplace – Credentials for Sale/ Hacking Services
Reference: Trend Micro Follow the Data: Dissecting Data Breaches and Debunking Myths
SecureWorks: Underground Hacker Markets
New Trend – Adversarial Learning
Reference: https://sarahjamielewis.com/posts/adversarial-machine-learning.html
ModelGenerate
new sample
Desired Outcome?
Evasion Success
Yes
No
ModelRegular Training sample
Desired Outcome?
PoisonedYes
Generate Mallicious
sample
