A Hybrid Framework Combining AI and Graph Theory for Advanced Malware Propagation Detection

Abstract

Collaborative learning is becoming an increasingly important tool for organizations in multi-enterprise ecosystems, where multiple stakeholders, including businesses, educational institutions, and research entities, collaborate to innovate and solve complex problems. However, such collaborations often face significant challenges related to data privacy, security, and trust between participants. This paper proposes a novel framework that integrates Artificial Intelligence (AI) techniques with secure collaborative learning to address these challenges. The framework is designed to provide a secure environment for data sharing, knowledge exchange, and model development without compromising sensitive information. AI algorithms are used to enhance the security of data during training and improve the accuracy and efficiency of collaborative learning processes. The framework incorporates encryption, federated learning, and differential privacy mechanisms to ensure the confidentiality of sensitive data while allowing participants to collaboratively train machine learning models. The paper explores the benefits of this AI-powered framework, including its ability to foster trust, reduce risks, and encourage participation in multi-enterprise collaborations. Real-world applications and potential use cases of the framework are also discussed, highlighting its relevance in industries such as healthcare, finance, and manufacturing. The paper concludes with a discussion on the future potential of AI-based secure collaborative learning frameworks in multi-enterprise ecosystems.