Researchers have recently noted [14, 27] the potential of fast poisoning attacks against DNS servers, which allows attackers to easily manipulate records in open recursive DNS resolvers. A vendor-wide upgrade mitigated but did not eliminate this attack. Further, existing DNS protection systems, including bailiwick-checking  and IDS-style filtration, do not stop this type of DNS poisoning. We therefore propose Anax, a DNS protection system that detects poisoned records in cache.
Our system can observe changes in cached DNS records, and applies machine learning to classify these updates as malicious or benign. We describe our classi- fication features and machine learning model selection process while noting that the proposed approach is easily integrated into existing local network protection systems. To evaluate Anax, we studied cache changes in a geographically diverse set of 300,000 open recursive DNS servers (ORDNSs) over an eight month period. Using hand-verified data as ground truth, evaluation of Anax showed a very low false positive rate (0.6% of all new resource records) and a high detection rate (91.9%).