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Intro

Leveraging intelligence across product lines

Microsoft's cloud security scale - Daily numbers

Textbook ML development

Traditional versus Transfer learning

Why transfer learning

Detecting malicious network activity in Azure Core Concept: Achieve transfer leaming by grouping similar tasks

Ensemble Tree Learning applications at Microsoft

Input data

Tree Ensembles - Algorithm

Tree Ensembles - Training

Tree Ensembles - Testing

Model performance and productization Model trained at regular intervals

Bonus Classifier can be used as an effective canary for emerging attacks

WannaCry Attack Timeline

Detecting Malicious PowerShell commands Core Concept: Transposing existing security problem into an already solved problem from another domain

PowerShell command lines - difficult to detect

Microsoft's Deep Learning toolkit (CNTK) applications

Deeper learning = representation learning

Technique overview

Neural Fuzzing Core Concept: Transposing existing security problem into an already solved problem from another domain

Seq2Seq Neural Architecture

Improved fuzzing intuition

readelf dataset example

Example readelf 2.28 model

Analysis by GDB exploitable plugin Target: Linux readelf 2.28

Readelf model performance over 48h and productization

Conclusion

Resources

Description:

Explore transfer learning applications in cybersecurity during this 46-minute RSA Conference talk by Mark Russinovich, Chief Technology Officer of Azure at Microsoft. Discover how machine learning algorithms developed for other domains can be repurposed to enhance cloud defense strategies. Learn about Microsoft's cloud security scale, traditional versus transfer learning approaches, and practical examples of applying transfer learning to detect malicious network activity in Azure and malicious PowerShell commands. Gain insights into ensemble tree learning, deep learning techniques, and neural fuzzing for improved security. Understand how these innovative approaches can be applied to your own cyber-defense efforts, with real-world examples such as the WannaCry attack timeline and readelf dataset analysis.

Transfer Learning - Repurposing ML Algorithms from Different Domains to Cloud Defense

RSA Conference

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#Conference Talks #RSA Conference #Information Security (InfoSec) #Cybersecurity #Computer Science #Machine Learning #Transfer Learning #Representation Learning

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