In this AI revolution, various Transformer-based models have successfully brought AI intelligence into everyday life and commercial applications through GPT-powered chatbots. This surge has led top-tier cybersecurity solutions to demonstrate that automated forensics and network management assistant chatbots can effectively support security investigations and response needs in practice, such as Defender Copilot. However, LLMs still struggle with their inherent hallucination issue, and their abilities can't fully address unexpected attacks from real-world threats.

Therefore, can we develop an AI detection engine that operates without human interaction, enabling 24/7 full-scope monitoring without the need for network administrators or forensic analysts? The vision is to deploy a pre-trained, on-premises AI agent capable of autonomously performing reverse engineering, reasoning, identification, and automated response in real time—without human intervention. This concept represents a new approach to next-generation endpoint detection and protection. Can we absorb the expertise of reverse engineers into a specialized AI model by leveraging large-scale samples?"

In this session, we will take the audience on a journey through academic research in pursuit of autonomous reverse engineering. We will explore how to transition from classic Attention-based Neural Machine Translation (NMT) models to AI agents with symbolic understanding and reasoning capabilities, ultimately training them as practical endpoint detection and reverse reasoning engines.