Combating Malware Traffic in Emerging Networks: A Collaborative Learning Approach

Identifying and mitigating malicious traffic poses a major challenge in the evolving field of network security. As threat profiles rapidly evolve, dynamic and adaptive methods are needed to match the swiftly changing attack patterns. This paper introduces a novel application of Federated Learning (FL) in the realm of malware traffic detection. As a distributed training framework, FL allows multiple network nodes to collaboratively learn a shared prediction model while keeping all the training data localized, thus ensuring data privacy and security. We present an effective malware identification and classification architecture utilizing Convolutional Neural Networks (CNN) within the FL framework distributed among network nodes. The paper details the design and implementation of this system, highlighting the integration of FL to harness the collective intelligence of diverse data sources without compromising data privacy. Our approach provides a scalable and efficient solution adaptable to diverse network environments. The results showcase the potential of FL in enhancing network security mechanisms, opening new avenues for combatting sophisticated cyber threats in an increasingly connected world.