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.

翻译：尽管解决互联网骨干网络中的黑洞故障至关重要，但骨干网络中缺乏有效的检测策略。这主要是因为以往的研究集中在移动自组织网络（MANETs）上，而MANETs在完全不同的动态、协议和拓扑结构下运行，导致其研究成果无法直接迁移到骨干网络。此外，在骨干网络中检测黑洞故障尤其具有挑战性。由于需要考虑多种多样的条件，这需要全面的网络数据，使得数据收集和分析远非直接了当。针对这一不足，本研究提出了一种新颖的骨干网络黑洞检测方法，该方法利用专用的下一代YANG数据模型并结合黑洞敏感度量矩阵（BHMM）分析。本文详细介绍了我们在ISP网络中选择和分析四种与黑洞检测相关的YANG模型的方法，重点聚焦于路由协议和ISP特定配置。基于这些模型得出的BHMM方法显示，检测准确率提高了10%，数据包投递率提高了13%，凸显了我们方法的有效性。此外，我们在两种不同的网络环境中评估了结合BHMM分析的机器学习方法：一个是商业ISP网络，另一个是纯科研网络拓扑。该评估也证明了我们方法的实际适用性，在两种环境下均取得了显著改进的预测结果。