COURSE SCHEDULE
Attendance type is IN-PERSON.
This 2 DAY DOJO takes place from April 22 (Tuesday) to April 23 (Wednesday)
SYLLABUS
Overview
This class is designed to introduce students to the most effective tools and techniques for applying cutting-edge deep learning–based artificial intelligence to cybersecurity tasks. By leveraging AI-driven automation, students will explore new ways to enhance security workflows and optimize vulnerability research.
We will take a deep dive into modern AI architectures, focusing on how deep learning models can assist in areas such as malware analysis and vulnerability research. The course will provide hands-on experience with large language models (LLMs) to solve real-world cybersecurity challenges and advanced security automation techniques.
Through practical exercises, students will gain proficiency in using AI to automate security tasks. By the end of the course, attendees will have the skills and knowledge to incorporate deep learning–based AI solutions into their cybersecurity workflows, enhancing both efficiency and effectiveness.
Who Should Attend
This class is meant for professional developers or security researchers looking to add deep learning artificial intelligence based automation to cybersecurity domains. Students wanting to learn a programmatic and tool driven approach to incorporating the latest artificial intelligence capabilities to their daily work will benefit from this course.
Key Learning Objectives
Gain a fundamental understanding of how modern AI models achieve capabilities such as text completion, data classification, summarization, and analytical tasks
Learn the tools and process for training new models, specializing and improving existing models, and evaluating model capabilities on downstream tasks
Leverage deep learning for tasks related to malware & vulnerability research
Prerequisite Knowledge
Students should be prepared to tackle challenging and diverse subject matter and be comfortable writing functions in python and C to complete exercises involving using python libraries or frameworks to write LLM enhanced tools and simple harnesses for C libraries. Attendees should also have basic experience with the high level applied topics such as reverse engineering, code auditing, and fuzzing on Linux.
Hardware / Software Requirements
This class will be using Python 3.10+ and LLVM/Clang on amd64 Linux. A preconfigured VMware Workstation VM will be provided as well as an amd64 Linux docker image. We will also use Google Collab notebooks for free online GPU resources. Students should have a working Google account with Google Collab access. Further instructions will be communicated prior to class.
Class Topics
Deep Learning Fundamentals
Model Architectures: CNN, LSTM, RNN, Transformers
Tokenizers and Embeddings
Embeddings and Vector Search
Retrieval Augmented Generation (RAG) Systems
Malware Classification and Clustering
Transformer Encoder and Embedding models
Malware detection using ML & Deep Learning
Fuzzing
LLM Prompting and Chain of Thought
Reasoning Models
Intro to fuzzing with libFuzzer
Fuzz harness generation with LLMs
Agents for Wargames & CTFs
Agentic systems with goal seeking, function calling, and tool use
Writing agents to play wargames and CTFs
INSTRUCTOR BIO:
Richard Johnson is a computer security specialist with a focus on fuzzing and software vulnerability analysis. Richard has been a training instructor since 2017 and is the founder of FUZZING IO, a research and development company offering professional training and consulting services. Richard offers over 20 years of professional expertise and leadership in the information security industry, previously Director of Security Research at Oracle Cloud leading software and hardware vulnerability research teams and at Cisco Talos as the founder of the VulnDev team that finds hundreds of zeroday vulnerabilities each year. Richard has delivered training and presented annually at premier industry conferences for over two decades including Black Hat, Defcon, OffensiveCon, RECON, CanSecWest, and many more.