• Nullcon Goa Sep 2022
  • Training
  • ML for Security and Security for ML

ML for Security and Security for ML

Nikhil Joshi

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Nikhil Joshi

Trainer Name: Nikhil Joshi
Title: ML for Security and Security for ML
Duration: 3 Days
Dates: Sept. 6, 2022 To Sept. 8, 2022


Machine learning / Deep learning is under exponential growth these days. Businesses, Academia, and tech enthusiasts are really hyped about trying out Deep learning to solve their problems. A lot of students, professionals, and researchers are driven to learn this new cool tech. Just like every other technology, ML comes with awesome applications topped with some serious implications.

So, join the 3 days expedition specially designed for security professionals to understand, build and hack Machine Learning applications. The course is divided into two parts, ML4SEC & SEC4ML. ML4SEC will focus on nitty-gritties of building ML applications. Then learn to hack them in the SEC4ML part.

Training level: Intermediate; Basic



Considering no prior knowledge of mathematics and ML, we will try to build the intuition behind algorithms. Attendees will go through hands-on experience in building ML-powered defensive and offensive security tools. An in-depth understanding of the entire ML pipeline is provided. Which consists of pre-processing data, building ML models, training and evaluating them, and using trained models for prediction. Well-known machine learning libraries like Tensorflow, Keras, Pytorch, sklearn, etc. will be used. In the end, you will be ready with end-to-end and ready to apply ML Gyan for security professionals.


This part will address the vulnerabilities (like Adversarial learning, Model stealing, Data poisoning, Model Inference, etc) in state-of-the-art machine learning methodologies. Lab material will consist of Vulnerable Machine Learning applications that can be exploited to provide a thorough understanding of discussed vulnerabilities. Possible mitigation to these vulnerabilities will also be discussed.



In this session, we will build up our 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, we 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?


In this session, we 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
  • Analyzing 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


  • Laptop with 8GB+ RAM
  • 20 GB space
  • Virtual box (latest version)
  • Any flavor of Linux is preferred over windows
  • Open mind made up for some intense mathemagic


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


  • 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
  • Students with a computer science background and a taste for ML and infosec


  • 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


  • Course slides and notes.
  • Precooked VM ready to run lab and exercise codes for post-training practice.
  • Starter code for future projects


  • Being an ML pro in two days
  • The heavy mathematical background of Machine Learning concepts

About Trainer

Nikhil Joshi is 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.

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