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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
The document discusses a conference held on September 1-2, 2016, focusing on advancements in fraud detection and adversarial learning. It presents various techniques for identifying identity theft, including the use of machine learning models and data analytics. The content emphasizes the evolving nature of fraud and the importance of robust data science methods in combating these threats.
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 toadversarial 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 om 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 ChildNameSpouse Name Bank Account Credit Card Number User Profile Electronic Record Who? ID Theft Definition Business Objectives
- 4. • Financial/Medical/Insurance IDTheft • Synthetic • Account take over (ATO) Common Type of ID Theft
- 5. Business Objectives Data Exploration/ Preparation DeploymentModeling Evaluation Fraud Definition Objectives Account Transaction Behavior ExternalData Feature Engineering Supervised Learning Unsupervised Learning Ensemble Model Performance Metrics Parameter Tuning Platform Testing Train vs Test Fraud Modeling
- 6. Random Forest SupportVector Machine (SVM) Deep Learning – Stacked denoising Autoencoder (SdA)UnsupervisedSupervised Multistage Ensemble Model Feature Extraction Boosting Feature Extraction - Ensemble
- 7. IDT NonIDT Selected 82 10 Not Selected 8 982 990 16 984 1,000 Determined By Model’s Performance
- 8. IDT NonIDT Selected 82 10 Not Selected 8 982 990 16 984 1,000 IDT Definition IDT Prevalence Estimate in Population
- 9. IDT NonIDT Selected 82 10 Not Selected 8 982 990 16 984 1,000 Unverifiable During the Operation
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- 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