  Despite the crucial importance of addressing Black Hole failures in Internet
backbone networks, effective detection strategies in backbone networks are
lacking. This is largely because previous research has been centered on Mobile
Ad-hoc Networks (MANETs), which operate under entirely different dynamics,
protocols, and topologies, making their findings not directly transferable to
backbone networks. Furthermore, detecting Black Hole failures in backbone
networks is particularly challenging. It requires a comprehensive range of
network data due to the wide variety of conditions that need to be considered,
making data collection and analysis far from straightforward. Addressing this
gap, our study introduces a novel approach for Black Hole detection in backbone
networks using specialized Yet Another Next Generation (YANG) data models with
Black Hole-sensitive Metric Matrix (BHMM) analysis. This paper details our
method of selecting and analyzing four YANG models relevant to Black Hole
detection in ISP networks, focusing on routing protocols and ISP-specific
configurations. Our BHMM approach derived from these models demonstrates a 10%
improvement in detection accuracy and a 13% increase in packet delivery rate,
highlighting the efficiency of our approach. Additionally, we evaluate the
Machine Learning approach leveraged with BHMM analysis in two different network
settings, a commercial ISP network, and a scientific research-only network
topology. This evaluation also demonstrates the practical applicability of our
method, yielding significantly improved prediction outcomes in both
environments.
