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