Angus Chen
Binary Defense, Director of Data Science
Actions
Angus Chen is a leader who fosters a culture of trust and is an experienced cybersecurity data scientist known for his ability to connect the dots. As DEFCON Goon and a board member, he contributes to communities and promotes diversity in all its forms. He is also a speaker who shares his knowledge extensively. With over 20 years of hands-on experience, Angus has developed, and implemented data science and deep tech solutions for a wide range of organizations, including MITRE, the Federal Reserve Board, and FINRA. He is a rock climber who looks for projects. He is the Director of Data Science at Binary Defense. He holds an MBA from IESE and a Master's in Applied and Computational Mathematics from Johns Hopkins University. Angus is also certified in CISSP, CCSP, and PMP.
Security AI
Welcome to Security AI! The goal of this course is to inform on how artificial intelligence is becoming one of the major tools in our security arsenal. The problem is that, unless mom you have a specific type of degree, you are at the mercy of product vendors,collaborators, ChatGPT, or search engines to understand these concepts. This course demystifies artificial intelligence and its relationships.
This is an interactive course, with the goal of teaching security professionals how to implement AI in order to obtain valuable insights. This course will encompass various topics including: machine learning (ML), and large-language models (LLMs). The combination of AI and security allows the security community to move our assumptions, opinions and beliefs into knowledge.
No previous experience is necessary. Background understanding programming is very helpful, specifically Python.
Module Title
00 Introduction to SecurityAI
01 History of AI
02 Introduction to AI
03 Difficulties of SecurityAI
04The Data Science Process
05 Introduction to Machine Learning
Unsupervised Learning - event log analysis for persistence
Case Study: Phishing Detection
GenerativeAI
Large Language Models
llama - synthetic event log generation
Case Study: Phishing Detection with GenerativeAI
Get your tickets: https://www.eventbrite.com/e/bsidesnova-2024-8-bit-games-sept-6-7-arlington-va-tickets-971314707437
AI-Driven Security
The goal of this course is to inform on how Artificial Intelligence is becoming one of the major tools in our security arsenal. The problem is that, unless you have a specific type of degree, you are at the mercy of product vendors, collaborators, ChatGPT, or search engines to understand these concepts. This course demystifies Artificial Intelligence and its relationships.
This interactive course will teach security professionals how to conduct data science techniques to manipulate and analyze security data to uncover valuable insights. The course will cover a few topics from data preparation, feature engineering and selection, exploratory data analysis, data visualization, machine learning, model evaluation and optimization and finally, implementing at scale.
No previous experience is necessary. Background understanding Python programming is helpful.
Course outline:
• Introduction to AI
• Applied AI to Cybersecurity
• Difficulties of AI-Driven Security
• Introduction to Machine Learning & Data Science
• A case study coding
• Walking through the case study coding exercise with a real-world data set
A Data Scientist and a Threat Hunter Walk into a Bar
In this talk, we'll explore the dynamic partnership between a data scientist and a threat hunter as they join forces to elevate their company's machine learning (ML) powered detection and response capabilities. By blending threat intelligence with advanced ML techniques, they were able to create capabilities to uncover unknown threats across various data sources. Attendees will gain an understanding on how to bring ML into their detection strategy, practical applications of machine learning for threat detection, and understanding how to layer it with attacker operations and threat intelligence to enhance detection strategies, and see real-world examples of these concepts in action.
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top