Metrics for community dynamics applied to unsupervised attacks detection
In Rencontres des jeunes chercheurs en intelligence artificielle
Abstract Attack detection in big networks has become a necessity. Yet, with the ever changing threat landscape and massive amount of data to handle, network intrusion detection systems (NIDS) end up being obsolete. Different machine-learning-based solutions have been developed to answer the detection problem for data with evolving statistical distributions. However, no approach has proved to be both scalable and robust to passing time. In this paper, we propose a scalable and unsupervised approach to detect behavioral patterns without prior knowledge on the nature of attacks.