October 28–31, 2019
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Generative malware outbreak detection

Sean Park (Trend Micro)
5:00pm5:40pm Wednesday, October 30, 2019
Location: Grand Ballroom A/B
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Researchers, engineers, and deep learning practitioners


Experienced Practitioner


Recently, several deep learning approaches have attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they’ve shown respectable performance on a large corpus of datasets, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available.

Sean Park demonstrates the effectiveness of the latent representations obtained through the adversarial autoencoder for malware outbreak detection. Using instruction sequence distribution mapped to a semantic latent vector, the model provides a highly effective neural signature that helps detecting variants of a previously identified malware within a campaign mutated with minor functional upgrade, function shuffling, or slightly modified obfuscations. Sean explains the effectiveness of generative adversarial autoencoders for static malware detection under outbreak situations where a single sample of a kind is available to detect similar in-the-wild samples. The model performance is evaluated over real-world macOS and Windows malware samples against traditional machine learning models.

Prerequisite knowledge

  • A basic understanding of TensorFlow and malware

What you'll learn

  • Discover the flexibility TensorFlow offers critical tools for cybersecurity against real-life malware threats
  • Understand how deep neural networks can generate next-generation neural signatures effective against dynamically morphing malware
  • Learn how neural signature changes the paradigm of cybersecurity
Photo of Sean Park

Sean Park

Trend Micro

Sean Park is a senior malware scientist in the Machine Learning Group at Trend Micro, as part of an elite team of researchers solving highly difficult problems in the battle against cybercrime. His main research focus is deep learning-based threat detection, including generative adversarial malware clustering, metamorphic malware detection using semantic hashing and Fourier transform, malicious URL detection with attention mechanism, macOS malware outbreak detection, semantic malicious script autoencoder, and heterogeneous neural networks for Android APK detection. Previously, he worked for Kaspersky, FireEye, Symantec, and Sophos. He also created a critical security system for banking malware at a top Australian bank.

  • O'Reilly
  • TensorFlow
  • Google Cloud
  • IBM
  • Databricks
  • Tensor Networks
  • VMware
  • Amazon Web Services
  • One Convergence
  • Quantiphi
  • Lambda Labs
  • Tech Mahindra
  • cnvrg.io
  • Determined AI
  • Inferencery
  • Manceps, Inc.
  • PerceptiLabs
  • Valohai

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