From fraudulence to adversarial learning จรัล งามวิโรจน์เจริญ chief data scientist and VP of Data Innovation Lab at Sertis, former lead data scientist of Booz Allen Hamilton
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From fraudulence to adversarial learning จรัล งามวิโรจน์เจริญ chief data scientist and VP of Data Innovation Lab at Sertis, former lead data scientist of Booz Allen Hamilton
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From fraudulence to adversarial learning จรัล งามวิโรจน์เจริญ chief data scientist and VP of Data Innovation Lab at Sertis, former lead data scientist of Booz Allen Hamilton
- 1. 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.
- 2. 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
- 3. Address National IDPhone Number Child NameSpouse Name Bank Account Credit Card Number User Profile Electronic Record Who? ID Theft Definition Business Objectives
- 4. • Financial/Medical/Insurance ID Theft • Synthetic • Account take over (ATO) Common Type of ID Theft
- 5. 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
- 6. Random Forest Support Vector Machine (SVM) Deep Learning – Stacked denoising Autoencoder (SdA)UnsupervisedSupervised Multistage Ensemble Model Feature Extraction Boosting Feature Extraction - Ensemble
- 7. IDT NonIDT Selected 8 2 10 Not Selected 8 982 990 16 984 1,000 Determined By Model’s Performance
- 8. IDT NonIDT Selected 8 2 10 Not Selected 8 982 990 16 984 1,000 IDT Definition IDT Prevalence Estimate in Population
- 9. IDT NonIDT Selected 8 2 10 Not Selected 8 982 990 16 984 1,000 Unverifiable During the Operation
- 11. Dark Web Marketplace – Credentials for Sale/ Hacking Services Reference: Trend Micro Follow the Data: Dissecting Data Breaches and Debunking Myths SecureWorks: Underground Hacker Markets
- 12. New Trend – Adversarial Learning Reference: https://sarahjamielewis.com/posts/adversarial-machine-learning.html Model Generate new sample Desired Outcome? Evasion Success Yes No Model Regular Training sample Desired Outcome? Poisoned Yes Generate Mallicious sample