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/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.
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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
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Address
National IDPhone Number
Child NameSpouse Name
Bank Account
Credit Card Number
User Profile
Electronic Record
Who?ID Theft Definition
Business Objectives
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• Financial/Medical/Insurance ID Theft
• Synthetic
• Account take over (ATO)
Common Type of ID Theft
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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
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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
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IDT NonIDT
Selected 8 2 10
Not Selected 8 982 990
16 984 1,000
Determined By Model’s Performance
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IDT NonIDT
Selected 8 2 10
Not Selected 8 982 990
16 984 1,000
IDT Definition IDT Prevalence Estimate in Population
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IDT NonIDT
Selected 8 2 10
Not Selected 8 982 990
16 984 1,000
Unverifiable
During the Operation
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http://manager.co.th/Daily/ViewNews.aspx?NewsID=9590000083749
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Dark Web Marketplace – Credentials for Sale/ Hacking Services
Reference: Trend Micro Follow the Data: Dissecting Data Breaches and Debunking Myths
SecureWorks: Underground Hacker Markets
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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