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Anomaly Detection on DNS Auths Root DNS, ccTLDs and DNS providers Team SchabeltierAnomalizers RIPE 74 Budapest, Hungary 2017-05-09 1/12 Team Members (alphabetically) Christian Doerr (TU Delft) Ella Titova

  1. Anomaly Detection on DNS Auths Root DNS, ccTLDs and DNS providers ✭ Team ✭✭✭✭✭✭ SchabeltierAnomalizers RIPE 74 Budapest, Hungary 2017-05-09 1/12

  2. Team Members (alphabetically) ◮ Christian Doerr (TU Delft) ◮ Ella Titova (VivaCell) ◮ Giovane Moura (SIDN Labs) ◮ Jan Harm Kuipers (University of Twente/SIDN Labs) ◮ Moritz M¨ uller (SIDN Labs/University of Twente) ◮ Ricardo Schmidt (University of Twente) ◮ Wouter de Vries (University of Twente) 2/12

  3. Main Problem Auth DNS Anomaly Detection ◮ How can we use Ripe Atlas data to automatically detect failures (anomalies) on Auth DNS (Roots, ccTLDs, etc...)? 3/12

  4. Step-by-step - CHAOS/RTT 1. Download Ripe datasets and parse them ◮ https://github.com/ripe-dns-anomaly/chaos ◮ parse-json.sh $startTime $endTime bins $mid ◮ start and end = timestamps ◮ bins= 600 (10minutes) ◮ mid = Ripe measurement ID 2. Then, run anomaly detection per letter and site: ◮ https: //github.com/ripe-dns-anomaly/anomalyDetector ◮ python letter-level-detector.py data/k-root-ddos-20151130.csv output/k-root-ddos-20151130-ad-hoc.csv 3. Then, it outputs anomalies per class type: ◮ https://github.com/ripe-dns-anomaly/ anomalyDetector/blob/master/README.md 4/12

  5. Step-by-step - Path 1. Download Ripe datasets and parse them ◮ https://github.com/ripe-dns-anomaly/traceroute ◮ python traceget.py --start $startTime --end $endTime --msmid $msmid ◮ start and end = timestamps ◮ msmid = atlas measurement id (5001 for K-root) 2. Then, convert to AS Path (plus IXPs): ◮ https://github.com/ripe-dns-anomaly/traceroute ◮ java -jar 3. Last step: anomaly detection and conversion to webformat (JSON) 5/12

  6. AS Graph - Path change during Nov 30 2015 Root DNS Attack 6/12

  7. Algorithms for Anomaly Detection ◮ See discussion on https://github.com/ripe-dns-anomaly/ anomalyDetector/blob/master/README.md ◮ Twitter’s robust TS analysis, ARIMA, ad-hoc ◮ We chose ad-hoc (ours) ◮ We need more time to evaluate the best one 7/12

  8. Overall reachability (K-root) 8/12

  9. Reachability London site (K-root) 9/12

  10. Path stability (K-root) 10/12

  11. ”Ready” to be used by others ◮ Others being: ccTLDs, Roots, etc. ◮ Requirement: Ripe Atlas measurements with chaos.id support and traceroute measurements ◮ Next: automate it to continuously probe it, detect and notify 11/12

  12. Resources ◮ GitHub: https://github.com/orgs/ripe-dns-anomaly/ ◮ Demo: https://ripe-dns-anomaly.github.io 12/12

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