AI 中文总结
本文提出ABDS指标，通过关联协议分歧位置与BGP拓扑自动分类原因，并发现安全设备本地主机响应签名，实现100%分类准确率，助力网络运营商自动化升级决策。
AI 中文摘要
网络运营商经常遇到协议分歧——即ICMP、TCP和UDP在路径上受到不同处理的情况。现有工具能够识别分歧发生的位置，但无法解释原因，迫使运营商进行人工调查，从而延误问题解决。我们提出两项贡献：（1）AS边界分歧评分（ABDS），一种将协议分歧位置与BGP拓扑相关联的指标，可自动将原因分类为路由策略驱动型或内部策略驱动型；（2）本地主机响应签名，一项实证发现，即安全设备在TTL超时消息中返回此http URL以掩盖其IP地址。我们针对8个类别（金融、政府、教育、医疗、科技、电商、媒体、国际）的496个目标评估了ABDS，并通过来自全球10多个地点的多视角验证确认了路由一致性。ABDS在已验证的分歧目标上实现了100%的分类准确率（50/50），显著优于多数类基线（54%，McNemar检验p < 0.001）。本地主机签名在识别安全设备方面实现了100%的精确率（18/18，95%置信区间：[82%, 100%]），涵盖包括政府（IRS、USPS、VA）、金融和医疗在内的9个类别。我们的开源Rust实现支持自动化升级决策，缩短了网络运营商的平均解决时间。
英文摘要
Network operators frequently encounter protocol divergence---where ICMP, TCP, and UDP experience different treatment along a path. Existing tools identify where divergence occurs but not why, forcing manual investigation that delays resolution. We present two contributions: (1) the AS-Boundary Divergence Score (ABDS), a metric that correlates protocol divergence locations with BGP topology to automatically classify causes as routing-policy-driven versus internal-policy-driven; and (2) the localhost response signature, an empirical finding that security appliances return 127.0.0.1 in TTL-exceeded messages to mask their IPs. We evaluate ABDS against 496 targets across 8 categories (Finance, Government, Education, Healthcare, Tech, E-commerce, Media, International), with multi-vantage validation from 10+ global locations confirming routing consistency. ABDS achieves 100% classification accuracy on verified divergent targets (50/50), significantly outperforming majority-class baseline (54%, McNemar's p < 0.001). The localhost signature achieves 100% precision (18/18, 95% CI: [82%, 100%]) in identifying security appliances across 9 categories including government (IRS, USPS, VA), finance, and healthcare. Our open-source Rust implementation enables automated escalation decisions, reducing mean-time-to-resolution for network operators.
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