ENTRADA: Enabling DNS Big Data Applications
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1 ENTRADA: Enabling DNS Big Data Applications Maarten Wullink - SIDN APWG ecrime 2016 June 2 nd Toronto
2 What if You have many TB s of network data? And you want to: 1. Store it efficiently 2. Query it efficiently (SQL with interactive response times) 3. Quickly test a large number of hypotheses on your data 4. Continuously keep adding new data
3 You could 1. Convert pcap to text format like csv and use Linux utilities 2. Run Hadoop MapReduce jobs on csv/pcap 3. Store it in a RDBMS 4. With most options it will be hard to scale and deliver interactive response times
4 What to do? Build your own data stream warehouse (DSW) ENTRADA is our open source Hadoop-based DSW (entrada.sidnlabs.nl) Analyze 50TB of converted pcap data in under 3.5 minutes using a small cluster Our main use case: network (DNS, TCP/IP, ICMP) analytics
5 ENTRADA ENhanced Top-Level Domain Resilience through Advanced Data Analysis ENTRADA'Applica8ons'and'Services' Support'Libraries' IMPALA' HDFS' Parquet' Workflow'Manager' DNS'Library' PCAP'Converter' ENTRADA'Pla+orm' Generic'components' ENTRADAJspecific'components' Name'Servers'
6 We are the TLD registry of the Netherlands (.nl) Use ENTRADA to further increase security and stability Operational for over 2 years Capturing data from.nl name servers 160 billion rows (DNS query+response tuple), 21 TB of data
7 More ENTRADA details For design choices and a performance evaluation, see our 2016 NOMS paper: ENTRADA: a High-Performance Network Traffic Data Streaming Warehouse, IEEE/ IFIP Network Operations and Management Symposium 2016 (NOMS 2016), Instanbul, Turkey See:
8 Example Use Cases Statistics (stats.sidnlabs.nl) Scientific research Insight for DNS operators Malicious domain detection Botnet client detection Measuring uptake of security
9 Malicious Domain Detection (1/2) Observation: New phishing domains have distinct query patterns Random Sample Jan Mar, 2015 Phishing Daily Queries Daily Queries Days After Registration Days After Registration G. Moura, M. Müller, M. Wullink, and C. Hesselman, ndews: a New Domains Early Warning System for TLDs, IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet 2016),
10 Malicious Domain Detection (2/2) Every day workflow Normal Newly Registered Domains Domain Characteristics Cluster Domains Suspicious Registry DB ENTRADA Σ PReq: popularity Σ PIPs: resolver diversity Σ PCC: country diversity Σ PASes: AS diversity Notify Registrar
11 Botnet Client Detection (1/2).nl Privacy Board Bot Shared (ISP) Resolver DetecBon Algoritm Privacy Policy ENTRADA Pla8orm Abusedesk Bot.nl auth Name server Bot
12 Botnet Client Detection (2/2)
13 Uptake of DKIM/DMARC (1/3) security standards DKIM (RFC 6376) and DMARC (RFC 7489) Approach: count standardized labels Where is DKIM/DMARC used most? select country,count(1) as total from dns.queries where qtype=16 and (qname like %_domainkey.% or qname like _dmarc.% ) and rcode=0 and ((year=2014 and month>6) or year=2015) group by country Use standard SQL for analysis
14 Uptake of DKIM/DMARC (2/3) Country # Queries Percentage US 208,533, IE 84,515, NL 79,052, BE 67,963, FI 9,112, RU 7,306, DE 7,119, GB 5,897, CN 5,446, DK 2,958, % of queries originate from top 4 countries
15 Uptake of DKIM/DMARC (3/3) Provider ASN # Queries Percentage Google AS ,465, Microsoft AS ,556, Unknown UNKN 15,788, AOL AS ,971, Yahoo AS ,83, Yahoo AS ,24, Yahoo AS ,150, Yahoo AS ,522, IDC China Tel AS ,520, Mail.ru AS ,659, % of queries originate from 4 large providers
16 Summary We have shown ENTRADA, a DSW built using open-source big data tools It enables quick hypothesis testing and application development using SQL We have shown real world example use cases ENTRADA can be extended to other use cases Download and contribute!
17 Future Work More DNS research in collaboration with research partners Develop data-driven applications and services based on ENTRADA Facilitate ENTRADA user community
18 Questions? Maarten Wullink Sr. Research entrada.sidnlabs.nl
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