Machine Learning Security Training

ML4SEC

In this session, Nikhil will build up the understanding of basic yet state-of-the-art machine learning algorithms. Discuss mathemagic behind why these models work the way they do. Build some smart Machine Learning applications and evaluate them. By the end, the participants will get an idea of how to solve a real-world problem using machine learning.

• Introduction to Machine learning
• Common use cases, where to use and where not to use machine learning.
• Introduction to different python libraries/packages like keras, tensorflow, sklearn
• Overview of how machine learning models are built and deployed in production.
• Understanding Mathematics and intuition behind used machine learning algorithms
• Supervised learning
• Linear regression, logistic regression, Neural nets, and similar classifiers
• Unsupervised learning
• Clustering algorithms like k-means
• Semi-supervised learning
• A brief introduction on data pre-processing with demo
• Cooking a dataset so that it can be consumed by discussed models.
• Feature engineering: Decreasing the dimensionality of the problem or adding more features to the dataset.
• Removing unnecessary data and handling different data types
• Dealing with incomplete data
• Applications of machine learning in the security domain with hands-on examples
• A detailed process of how to leverage previously discussed knowledge to build applications in defensive as well as offensive security.
• Image classifier using deep learning.
• Defensive sec:
  • Web access firewalls
  •  Intrusion detection systems
  • Malware detection engine
• Offensive sec:
  • Machine learning for phishing.
  • Machine learning for fuzzing.
• Evaluate the built models using different evaluation parameters.
• Now that we have made our systems “Intelligent”, is it possible to fool them? Are these applications hackable?

SEC4ML

In this session, the particpants will have a deeper look at different flaws in how ML/DL algorithms are implemented. Hands-on examples explaining and attacking such vulnerable implementations. Also, discussion on possible mitigation.

• A brief introduction to vulnerabilities in Machine Learning
• Discussion on various ways of compromising machine learning apps
• Adversarial learning Attacks
• Introduction and mathematical intuition behind the existence of this flaw
• Demo and hands-on practice of fooling very accurate state-of-the art Image classifiers
• Analysing why this attack works
• Possible mitigation
• Model stealing Attacks.
• How proprietary ML models can be stolen by an attacker, making him/her use the models for FREE.
• Stealing offline ML models that are deployed on devices with installer packages.
• Stealing models that are deployed on the cloud with restricted access via APIs.
• Demo
• Adversarial Attack on face-recognition application
• Model Skewing and data poisoning attacks.
• How and why this attack works.
• Hands-on example of bypassing ML-based 99.99% accurate Spam Filters
• Possible Mitigation
• Discussion on other lesser addressed vulnerabilities and real-world impact.
• CTF challenge focuses on one of the discussed vulnerabilities.

Who should attend?

• Machine learning enthusiasts and professionals.
• Security researchers and pentesters looking forward to implementing ML/DL in their research.
• Pentesters willing to explore new ways to pentest Machine learning applications.

What to expect?

• Thorough understanding of basic machine learning methodologies
• Hands-on practice in specially crafted labs for ML and Infosec enthusiasts
• End-to-end and ready to apply ML knowledge for security professionals
• Good understanding of Machine learning vulnerabilities
• Hands-on experience with well-known machine learning libraries
• Lab material for post-course practice

What NOT to expect

• Being an ML Pro in 2 days
• The heavy mathematical background of Machine Learning concepts

Prerequisites

• Basic knowledge of python is good to have but not required.
• Basic of Linux and VirtualBox

Trainer Profile

Nikhil Joshi

Nikhil Joshi is an AI Security Researcher. He is currently working on implementations of ML in offensive and defensive security products. He has orchestrated methodologies to pen-test Machine Learning applications against ML-specific vulnerabilities and loves to explore new ways to hack ML-powered applications. Parallelly Nikhil’s research is focused on security implications in Deep Learning applications such as Adversarial Learning, Model stealing attacks, Data poisoning, etc.

Nikhil is an active member of local Data Science and Security groups and has delivered multiple talks and workshops. He has spoken at HITB Amsterdam, PhDays Russia, and IEEE conferences. And trainer at the Nullnon and Troopers. Being an Applied Mathematics enthusiast, recent advances in Machine Learning and its applications in security, behavioral science, and telecom are of major interest to Nikhil.