Workshops on Guy Barnhart-Magen

Adverserial Machine Learning Workshop

Mon, 04 Nov 2019 14:53:09 +0200

Overview of Machine Learning Tasks

Supervised Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning
Real-world applications of Machine Learning
Lab 1: Deep Learning for face recognition
Machine Learning empirical process
Theoretical model of Machine Learning
Application of Machine Learning in Cybersecurity
Lab 2: Support Vector Machine for IoT malware threat hunting

The Machine Learning Threat Model

The ML attack surface
Adversarial capabilities
Adversarial objectives
ML threat modelling
Lab 3: Identifying attack surface and threat modelling of a Deep Learning agent for traffic sign detection
ML training in adversarial settings
ML inference in adversarial settings
ML Differential Privacy and model thefts
Lab 4: Data exfiltration from a trained Decision Tree model

Adversarial Poisoning Attacks

Adversarial poisoning attacks methodology
Poisoning attacks techniques
Lab 5: Poisoning attacks against a text-recognition Deep Learning agent

Adversarial Evasion Attacks

Adversarial evasion attacks methodology
Evasion attacks techniques
Lab 6: Evading an object recognition Deep Learning agent

Adversarial Attacks on Malware Detection Systems

Machine learning for malware analysis
Poisoning and evasion attacks against ML-based malware detection systems
Lab 7: Evading a deep convolutional neural network agent for malware detection

ML Differential Privacy and Model Thefts

Foundations of differential privacy
Data inference and model theft attacks
Lab 8: Stealing an overfitted machine learning model to extract credit card information

Penetration Testing of ML models

Penetration testing methodology of ML engines
Input sanitization of ML models
Payload injection attacks
Lab 9: Input sanitization testing of a ML image recognition system using anomaly
An adversarial learning defense reference model
Detecting adversarial attacks
Preventing techniques for adversarial attacks
Preserving privacy ML models
The ML deception Defense-in-Depth (ML-DiD) Frameworks
Lab 10: architecting a defense in depth model in an AI-centered enterprise

Machine Learning Security Workshop

Mon, 04 Nov 2019 14:53:09 +0200

Course Objectives

At the end of the course, participants will:

Understand the security threat landscape
Understand the top security issues revolving around machine learning
Understand how to take preventive measures
Learn about the Secure Development Lifecycle (SDL) for machine learning products
Gain knowledge on the security considerations when deploying production models

Course Overview

With the explosive growth of machine learning applications and products, the question of their security touchpoint is becoming a major interest area for many organizations. Machine learning security covers both how such products affect the security posture of the organization, and what threats they bring to such a system, as well as how to protect such systems from adversaries. This course outlines the state-of-the-art in machine learning security and how the topic has evolved. It is intended for developers and managers to make strategic decisions for their machine learning products as both a vendor and a customer.

IoT Security

Mon, 04 Nov 2019 14:53:02 +0200

Course Objectives

At the end of the course, participants will:

Understand the security threat landscape and how to take preventive measures
Understand the top ten security issues revolving around IoT
Learn about the Secure Development Lifecycle (SDL) of IoT products
Learn about the dependencies and enrollment challenges of IoT products
Gain knowledge on the security considerations regarding verticals of connected devices
Learn from real-life examples and case studies in IoT Security

Course Overview

The growth and central role that the Internet of Things play in the technology world is a proven fact. IoT is bringing the network to the physical aspects of our lives; our cars, utilities, homes, cities, industries and more. With such influence, IoT security is the main concern for the whole industry of IoT stakeholders, from developers to service providers, customers, and end-users. This course outlines the state-of-the-art prevailing practices for IoT security and how this topic is evolving. It is intended for IoT developers and managers to make a strategic decision for their IoT products both as a vendor and as a customer.

Applied Cryptography Workshop

Mon, 04 Nov 2019 14:52:25 +0200

Course Objectives

At the end of the course, participants will:

Understand the basic building blocks of cryptography
Understand how basic blocks are used to build secure protocols
Understand weaknesses and attacks against protocols
Understand how cryptographic attacks are mounted through several real-life examples
Learn about real-world cryptographic attacks
Review current known issues in cryptography
Analyse case studies of a product with failed cryptographic implementations or design decisions
Understand the current stage of cryptographic building blocks and how they are affected by quantum computing

Course Overview

Linux Hardening - Made Easy

Thu, 08 Aug 2019 00:00:00 +0000

You can find the abstract below

Please Prepare for the Workshop

We wIll base our workshop on an ubuntu image, which I will link below. I recommend using VirtualBox VM - which is what I use.

This VM will have a basic OS, a web server and some files we would like to protect.

I am also using Ubuntu as my host, with ZSH - but will explain anything that looks funny :-)

CV Workshop

Wed, 18 Oct 2017 00:00:00 +0000

Some basic advice for you CV:

Place a short paragraph at the top of your CV explainging what added value do you bring to a position, based on all the work experience you have so far (from all positions)
Explain what did you do at each position you held and what value did you bring - not the job description
Include in your CV keywords that are relevant to the position you are applying to
Keep your CV clean with the most important information at the top of the page - this is your most valuable real estate on your CVs, the area with the highest chance of being read (and where decision to read further are made)
In general, for most positions your academic background is not as important as you think, push it to the bottom of your CV

This might merit a post by itself one day

Stealing Bitcoins

Wed, 18 Oct 2017 00:00:00 +0000

A code-along workshop demonstrating an attack on bitcoin and how to exploit cryptographic weaknesses to still bitcoin