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Making & Breaking Machine Learning Systems

Clarence Chio, Anto Joseph | March 20 - 21



Overview

Making & Breaking Machine Learning Systems is a fast paced session on machine learning from the Infosec professional's point of view. The class is designed with the goal of providing students with a hands-on introduction to machine learning concepts and systems, as well as making and breaking security applications powered by machine learning.

The lab session is designed with security use-cases in mind, since using machine learning in security is very different from using it in other situations. Students will get first hand experience at cleaning data, implementing machine learning security programs, and performing penetration tests of these systems.

Each attendee will be provided with a comprehensive virtual machine programming environment that is preconfigured for the tasks in the class, as well as any future machine learning experimentation and development that they will do. This environment consist of all of the most essential machine learning libraries and programming environments friendly to even novices at machine learning.

At the end of the class, students will be put through a CTF challenge that will test the machine learning development and exploitation skills that they have learned over the course in a realistic environment.

Who Should Take this Course

  • Security Professionals
  • Web Application Pentesters
  • Software/application developers
  • People interested to start using machine learning for security

Student Requirements

  • Basic familiarity with Linux
  • Python scripting knowledge is a plus, but not essential

What Students Should Bring

Laptop with:
  • Latest version of VirtualBox Installed
  • Administrative access on your laptop with external USB allowed
  • At least 20 GB free hard disk space
  • At least 8 GB RAM (the more the better)

What Students Will Be Provided With

  • Copy of O'Reilly Media book "Machine Learning & Security" (http://a.co/6jHo5Lv)
  • Slides
  • USB stick with 1) VM with all material used in class + much more, exploratory material that we will not cover in class 2) Additional notes 3) Preconfigured environment for machine learning research and experimentation

Trainers

Clarence Chio graduated with a B.S. and M.S. in Computer Science from Stanford within 4 years, specializing in data mining and artificial intelligence. He is the co-author of the O'Reilly book "Machine Learning and Security", (https://goo.gl/JEnnnh) and is currently the co-founder of an AI security company, KaiTrust. Clarence has been a speaker/trainer on Machine Learning and Security at over 30 conferences and meetups across more than 12 countries. (https://goo.gl/pSsD6D) He is also the founder and organizer of the 'Data Mining for Cyber Security' meetup group, the largest gathering of security data scientists in the San Francisco Bay Area.

Anto Joseph is a Security Engineer for Intel. He has 4 years of corporate experience in developing and advocating security in Machine Learning and Systems in Mobile and Web Platforms . He is very passionate about exploring new ideas in these areas and has been a presenter and trainer at various security conferences including BH USA 2016, Defcon 24, BruCon , HackInParis, HITB Amsterdam , NullCon , GroundZero , c0c0n , XorConf and more. He is an active contributor to many open-source projects and some of his work is available at https://github.com/antojoseph.