Abstract
Cybersecurity analysis leading to deterrence of cybercrime requires processing thousands to billions of digital signals per second. Those signals must be accurately comprehended, forensically preserved then used to detect and investigate potential cybercrime. The work products must not only assist the investigators but must be translated into language that non-technical lay audiences including judges, lawyers and jurors can understand.
This presentation explores how generative artificial intelligence (GenAI), natural language processing (NLP), graph-theory and artificial narrow intelligence (ANI) can play a role in delivering these outcomes.
The session includes demonstrations of opensource toolkits, datasets and models designed to assist in this work.
Background
Since 2016, WitFoo has researched how artificial intelligence (AI) can be used to synthesize human expertise at multi-Terabyte data rates required in cybersecurity analytics. This session includes a summary of lessons learned from that research concerning analytic modeling.
Objectives
- Learn how to build a dataset and train a generative AI model learn it using the ArtiFish toolkit.
- Understand the strengths and weaknesses of GenAI, NLP, ANI and Graph Theory in cybersecurity analysis.
- Examine the impact of triaging digital signals on effective analysis.
- Understand how generative AI can be an effective tool in translating cybersecurity analytic data to non-technical audiences.
