How I Learned to Stop Worrying and Love Building Data Products
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Most people think a successful data product requires just three things: data, the right algorithm, and good execution. But as anyone who’s tried to create one knows, an effective product requires much more. In this talk, Dr. Correa Bahnsen will share his successes—and failures—in building data products for information security, and why an isolated data science team is a recipe for failure.
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How I Learned to Stop Worrying and Love Building Data Products
- 1. Behind the Scenes in Building Data Products From Data Science to Data Products Experiences in Information Security Alejandro Correa Bahnsen, PhD Chief Data Scientist & Head of Research acorrea@easysol.net
- 2. Who am I? Chief Data Scientist at Easy Solutions Industrial Engineer PhD in Machine Learning from Luxembourg University Scikit-Learn contributor Organizer of Science Bogota Meetups 2
- 3. AboutEasySolutions® 3 A leading global provider of electronic fraud prevention for financial institutions and enterprise customers 430+ customers In 30 countries 115 million Users protected 30 billion Online connections monitored Industry recognition
- 4. Aims of this talk Discuss what makes a data science project successful 4
- 5. What is Data Science? 5
- 6. What is Data Science? 6
- 9. 9
- 10. Those are the pillars of data science: computing, statistics, mathematics, and quantitative disciplines, combined to analyze data for better decision making 10 DataScienceIstheIntersectionofHacking Skills,Math&StatisticsKnowledgeand SubstantiveExpertise
- 11. Hacking Skills Ability to build things and find clever solutions to problems • Programming/Coding: Python and R (and others) • Databases: MySQL, PostgreSQL, Cassandra, MongoDB and CouchDB. • Visualization: D3, Tableau, Qlikview and Markdown. • Big Data: Hadoop, MapReduce and Spark. 11
- 13. Math & Statistics Being able understand the right solution to each problem • Linear algebra: Matrix manipulation • Machine Learning: Random Forests, SVM, Boosting • Descriptive statistics: Describe, Cluster • Statistical inference: Generate new knowledge 13
- 14. Substantive Expertise Ability to ask good questions requires domain understanding, that’s why a data scientist can’t create data based solutions without a good industry knowledge • Is this A or B or C? (classification) • Is this weird? (anomaly detection) • How much/how many? (regression) • How is it organized? (clustering) • What should I do next? (reinforcement learning) 14
- 15. Building Data Products In Information Security 15
- 16. Research/ DataScienceSpectrum 16 • Maybe someday, someone can use this Basic Research • I might be able to use this Applied Research • I can use this (sometimes) Working Prototype • Software engineers can use thisQuality Code • People can use this Tool or Service Innovation practicality
- 17. TotalFraudProtection Discuss what makes a data science project successful 17
- 18. 18 Risk Based Authentication Phishing URL Classification Phishing Brand ID Fraud Detection 19h risk = 10 9h risk = 95 HTML Injection Biometrics
- 19. 19 Phishing
- 21. WhyPhishingDetectionisHard 21 Original Website Only Using Images Subtle Changes
- 24. IdealPhishingDetectionSystem - Issues Issues with full content analysis: • Time consuming • Impractical to process millions of websites per day • Hard to implement for small devices 24
- 27. Databaseof URLs 27 1,000,000 Phishing URLs from PhishTank http://moviesjingle.com/auto/163.com/index.php 1,000,000 Legitimate URLs from Common Crawl http://paypal.com.update.account.toughbook.cl/8a30e847925afc597516 1aeabe8930f1/?cmd=_home&dispatch=d09b78f5812945a73610edf38 http://msystemtech.ru/components/com_users/Italy/zz/Login.php?run= _login-submit&session=68bbd43c854147324d77872062349924 https://www.sanfordhealth.org/ChildrensHealth/Article/73980 http://www.grahamleader.com/ci_25029538/these-are-5-worst-super- bowl-halftime-shows&defid=1634182 http://www.carolinaguesthouse.co.uk/onlinebooking/?industrytype=1& startdate=2013-09-05&nights=2&location&productid=25d47a24-6b74
- 28. URLLexicalandStatisticalFrequencies 28 http://www.secure.paypal.com.papaya.com/secure_login.php URL length Alexa Ranking Path length URL Entropy # of .com Punctuation count TLD count Is IP? Euclidean distance KS & KL distance Is It Phishing?
- 29. URLLexicalandStatisticalFrequencies Results: 29 3-Fold CV Accuracy Recall Precision Average 93.47% 93.28% 93.64% Deviation 0.01% 0.02% 0.03%
- 31. MODELING PHISHING URLS WITH RECURRENT NEURAL NETWORKS 31
- 33. 33 Short term dependencies are easy long term … TheProblemofLong-TermDependencies
- 34. 34 RNN contains a single layer LSTM contains four interacting layers Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Long-ShortTermMemoryNetworksLSTM
- 35. Long-ShortTermMemoryNetworks 35 URL h t t p : / / w w w . p a p a y a . c o m One hot Encoding … … … … … … … … … … … … … … … … … … … … … Embedding 3.2 1.2 … 1.7 6.4 2.3 … 2.6 6.4 3.0 … 1.7 3.4 2.6 … 3.4 2.6 3.8 … 2.6 3.5 3.2 … 6.4 1.7 4.2 … 6.4 8.6 2.4 … 6.4 4.3 2.9 … 6.4 2.2 3.4 … 3.4 3.2 2.6 … 2.6 4.2 2.2 … 3.5 2.4 3.2 … 1.7 2.9 1.7 … 8.6 3.0 6.4 … 2.6 2.6 6.4 … 3.8 3.8 3.4 … 3.2 3.3 2.6 … 2.2 3.1 2.2 … 2.9 1.8 3.2 … 3.0 2.5 6.4 … 2.6 LSTM LSTM LSTM LSTM Sigmoid …
- 36. ModelsComparison 36 Model Random Forest Long-Short Term Memory Network Memory Consumption (MB) 289 0.56 Evaluation Time (URLs per sec) 942 281 Training Time (minutes) 2.95 238.7 Accuracy 93.7% 98.7%
- 37. 37
- 38. 38 1. Lets build Swordphish 3. Random Forest Classifier2. Data Collection 4. API 5. Product Evaluation 6. Recurrent Neural Networks 7. Distributed API 8. Port to C++ 9. Sales & Marketing 30 % 50 % 20 % Total Effort
- 39. 39
- 41. BrandID- Scope Given a phishing attack, determine the targeted bank
- 42. BrandID- Scope • Create a learning ML engine to label attacks against any brand • Not limited to current customers or known layouts • Apply ML techniques to extract knowledge • Enhance predictive capabilities
- 43. BrandID- Issues 1000’s of phishing attacks per hour detected worldwide
- 44. BrandID–Issues Target is not always straightforward
- 45. BrandID–Issues Target is not always straightforward
- 46. BrandIDArchitecture 1. Get Phishing Site Info 2. Analyze Images 3. Analyze Text 4. HTML Structure 5. Machine Learning Classifier 46
- 47. 1. GetPhishingSiteInfo 1. Screenshot 2. Page Text 3. HTML Code 47
- 49. 1. GetPhishingSiteInfo Splash takes 5s to render one URL BrandID receives 33,000 URLs per day It would take 4.6 days to process one day of URLs It’s expected to grow up to 1,000,000 49
- 51. Transfer Learning and Siamese Networks Main idea: find a function that maps input patterns into a target space such that a simple distance in the target space (say the Euclidean distance) approximates the “semantic” distance in the input space .84 2. AnalyzeImages
- 52. 3. AnalyzeText OCR with Leptonica 52
- 58. BrandIDArchitecture 1. Get Phishing Site Info 2. Analyze Images 3. Analyze Text 4. HTML Structure 5. Machine Learning Classifier 58
- 59. 59 Business Case Random Forest Classifier Data Collection Product Evaluation Image Analysis Distributed API Splash JS 10 % 50 % 40 % Total Effort NLP Spark AKKA Transfer Learning HTML Analysis
- 60. At the end of the day, there is much more to Data Products than just Machine Learning. 60
- 61. Any questions or comments, please let me know. Alejandro Correa Bahnsen, PhD Chief Data Scientist & Head of Research acorrea@easysol.net Thank you!