Research Area:  Machine Learning

Abstract:

The wide adoption of interconnected services leads to the creation of supportive solutions and business opportunities. Conversely, this new paradigm is targeted by malicious activities, aiming to compromise systems’ confidentiality, integrity, and availability. However, advanced methods lack contextual awareness, which prevents their deployment to real-world systems. Considering that the process of making informed decisions stems from the expertise of analysts based on their experience, the use of cybersecurity domain knowledge has the potential to improve Deep Learning and Deep Reinforcement Learning operations in real scenarios. Therefore, the main goal of this research is to study and evaluate the use of Knowledge Infused Learning in the context of automated threat defense. We define how cybersecurity domain knowledge can be infused into Deep Learning and Reinforcement Learning, highlighting the main challenges and benefits. Besides, we present a roadmap to apply domain knowledge for red and blue teaming activities and discuss the implications of Knowledge Infused Learning in explainability, and actionable reporting. Finally, we list the open challenges to guide the development of next-generation security solutions.

Keywords:  

Author(s) Name:  Sourena Khanzadeh, Euclides Carlos Pinto Neto, Shahrear Iqbal, Manar Alalfi & Scott Buffett

Publisher name:  Springer

DOI:  10.1007/s10207-025-00987-4

Volume Information:  Volume 24 , (2025)

Paper Link:   https://link.springer.com/article/10.1007/s10207-025-00987-4