DNSSM: A Large Scale Passive DNS Security Monitoring Framework
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1 16/04/12 DNSSM: A Large Scale Passive DNS Security Monitoring Framework Samuel Marchal, Jérôme François, Cynthia Wagner, Radu State, Alexandre Dulaunoy, Thomas Engel, Olivier Festor
2 2 / 18 Outline 1 Motivation 2 Solution 3 Experiments and Results 4 Conclusion
3 3 / 18 Outline 1 Motivation 2 Solution 3 Experiments and Results 4 Conclusion
4 4 / 18 Overview of DNS DNS (Domain Name System) is the service that maps a domain name to its associated IP addresses = DNS is the service that allows to find information about a domain : A : IPv4 address AAAA : IPv6 address MX : Mail server NS : Authoritative DNS server TXT : any information
5 5 / 18 DNS: Why DNS monitoring? critical Internet service threats: cache poisoning, typosquatting, DNS tunnelling, fast/double-flux enhance: phishing, botnet C&C communications, covered channel communications etc. Patterns in DNS packet fields and DNS querying behavior Passive DNS monitoring to detect: worm infected hosts malicious backdoor communication botnet participating hosts phishing websites hosting
6 6 / 18 Existing solutions Mainly use supervised classification techniques SVM, tree, rules, etc. require malicious data for training Targeted identification of malicious domains C&C communication involved domains Phishing domains Spamming domains etc.
7 7 / 18 Outline 1 Motivation 2 Solution 3 Experiments and Results 4 Conclusion
8 8 / 18 Clustering Automated clustering technique for online analysis No previous knowledge Group domains regarding their activity DNS information Domain activity Disclose the raise of new threats K-means clustering 10 relevant features
9 9 / 18 Features For each domain observed: Number of IP addresses IP scattering : entropy based and position weighted mean TTL Requests count Period of observation Requests per hour Name servers count Number of subdomains Blacklisted flag
10 User interface DNSSM is an approach for automated analysis of DNS (passive traffic) Manual assistance in tracking anomalies: Feed with cap file All DNS packet fields extracted MySQL database storage model Web interface Fast and efficient mining functions Integrates with existing blacklist tools to assist in tagging data Detection of fast/double flux domains, DNS tunnelling, etc. Freely downloadable at: /kit_dns_anomalies.tar.gz 10 / 18
11 11 / 18 Architecture
12 12 / 18 Outline 1 Motivation 2 Solution 3 Experiments and Results 4 Conclusion
13 Experiments 2 datasets ( location, type of network, users, quantity) Automatic results from k-means: 8 clusters exhibiting different properties Cluster 5: apple.com, amazon.fr, adobe.com(highly popular websites) 13 / 18
14 14 / 18 Results Cluster 6: google.com. skype.com, facebook.com (higly popular web sites) Cluster 7: tradedoubler.com, doubleclick.net, quantcast.com (user tracking) Cluster 3: akamai, cloudfront.net (CDN)
15 15 / 18 Results Cluster 0: small websites with low popularity
16 16 / 18 Outline 1 Motivation 2 Solution 3 Experiments and Results 4 Conclusion
17 17 / 18 Conclusion Passive DNS monitoring solution Analysis of domain names activity Relevant data mining algorithm (unsupervised clustering techniques) Efficiency proved on two different datasets Freely downloadable interface Applications: Investigate cyber security fraud Debug DNS deployment Penetration testing
18 16/04/12 DNSSM: A Large Scale Passive DNS Security Monitoring Framework Samuel Marchal, Jérôme François, Cynthia Wagner, Radu State, Alexandre Dulaunoy, Thomas Engel, Olivier Festor
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