Making & Breaking Machine Learning Systems

From March 20, 2017 to March 21, 2017

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 handson 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 usecases 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.

What to Bring

Latest version of VirtualBox Installed
Administrative access on your laptop with external USB allowed
At least 20 GB free hard disk space
At least 4 GB RAM (the more the better)

Prerequisites

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

Who Should Attend?

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

What to expect?

Familiarizing yourself with popular machine learning algorithms and how to adapt these for different problems
How to clean and sanitize data using powerful data processing libraries in Python
How to build a spam classifier and online anomaly detection system in Python
How to do performance evaluations of machine learning classifiers
Examples for using machine learning in intrusion detection, botnet detection, phishing detection, web vulnerability analysis, malware classification, and behavioural analysis
Perform tuning of machine learning systems to improve classification/detection results
Perform security evaluations and penetration tests on machine learning systems
- Fuzzing machine learning classifiers
How to avoid vulnerabilities in machine learning system and algorithm design
How to use Apache Spark to design scalable and distributed realtime machine learning systems
Write your own machine learning captcha solver

What not to expect?

To be a machine learning expert in just two days. This training will impart you all the necessary skills to start building security software using machine learning and teach the lesser known ways of exploiting such systems. Students need to put in further work and use the skills learnt in the class to continue their explorations in machine learning and keep up with the latest developments in this fast evolving field.

Clarence Chio

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 in the process of authoring the Apress/Springer book “Machine Learning Systems Under the Hood”, and currently works as a Security Research Engineer at Shape Security, building a product that protects high valued web assets from automated attacks. At Shape, he works on the data analysis systems used to tackle this problem. Clarence spoke on Machine Learning and Security at DEF CON 24, GeekPwn Shanghai, PHDays Moscow, BSides Las Vegas and NYC, Code Blue Tokyo, SecTor Toronto, GrrCon Michigan, Hack in Paris, QCon San Francisco, and DeepSec Vienna (20152016). He had been a community speaker with Intel, and is also the founder and organizer of the ‘Data Mining for Cyber Security’ meet-up group, the largest gathering of security data scientists in the San Francisco Bay Area.

Anto Joseph

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 opensource projects and some of his work is available at https://github.com/antojoseph.