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Security components of the CERN farm nodes

Security components of the CERN farm nodes

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Security components of the CERN farm nodes

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  1. Security componentsof the CERN farm nodes Vladimír Bahyl CERN - IT/FIO Vladimir.Bahyl@cern.ch Presented by Thorsten Kleinwort

  2. Outline • Current state • Our typical system • Possible risks • Protection methods • Security related • Against denial of service • Conclusion Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  3. A typical farm node • 2 CPUs / 1 GB RAM / 20 GB disk • 100 Mbit network • CERN RedHat Linux 7.3.3 • 50-70 users • 70 primary interactive nodes Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  4. Risks • Security related: • Exploits to the system to get root • Services started on unprivileged ports • System can be used to • scan other nodes • originate spam • Denial of service: • “Heavy” processes started • Disk filled by “runaway” jobs Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  5. Our protection methods • Keep the system secure and up-to-date (with CDB & SPMA) • Log more verbosely than default • Collect the logs centrally • Scan for certain patterns in the logs • Keep the system accounting • Provide secure access methods only • Transfer sensitive information securely • E.g. password files – but in general anything; use GPG for encryption • Monitor the current state • Disk – quota is enabled • CPU usage – beniced daemon • Incident reaction – as quick as possible • No later than the next working day – compromised account is blocked Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  6. Log more • It is always good to have more information to go back to in case of a need • Daemons are configured to log as much data as is convenient • portmap -v • netlog – the ultimate kernel module • logs TCP activity • outputs a line whenever a listening socket or an incoming or outgoing connection is established • it logs the program concerned, the session id, process id and user id • it also logs connection details (protocol, local/remote addresses and ports • provides extremely useful data in forensic investigation Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  7. Netlog example • Incoming connections: Oct 14 21:49:43 node kernel: netlog: info: connect start TCP 172.17.35.98:44073 <- 192.168.161.90:52453 31440 31476 240 pmg_agent Oct 14 21:49:43 node kernel: netlog: info: connect stop TCP 172.17.35.98:44073 <- 192.168.161.90:52453 31440 31476 240 pmg_agent • Outgoing connections: Oct 14 18:18:49 node kernel: netlog: info: connect start TCP 172.17.35.98:34434 -> 192.168.11.10:80 22163 23183 1648 wget Oct 14 18:18:52 node kernel: netlog: info: connect stop TCP 172.17.35.98:34434 -> 192.168.11.10:80 22163 23183 1648 wget Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  8. Collect the logs • Make sure that all daemons log via the syslog facility • Combine the logs in a single file • Option in /etc/syslog.conf • Process log files locally on each node • Combine the connection data • Remove uninteresting information • E.g. node boot messages • Compress • Forward to a central place = Oracle database • Do it in regular intervals to prevent loss of data (~ every hour) Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  9. Scan for certain patterns • For example: • IRC activity • IRC servers violate CERN’s computing rules • SUID ptrace exploit attempts 2003/07/23-13:10:38 Uu ? node.cern.ch[172.17.35.98] kernel request_module[net-pf-14]: waitpid(28284,...) failed, errno 512 2002/01/31-01:46:23 Uu ? node.cern.ch[172.17.35.98] kernel ptrcchk: uid=19201 tried ptrace on suid/sgid file /usr/bin/passwd • Generated by a proprietary kernel module • network sniffer 2003/10/03-17:44:54 Uu ? node.cern.ch[172.17.35.98] kernel device eth0 entered promiscuous mode • repeated login failures • etc. • This data can use used together with network IDS results Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  10. Keep system accounting • Also very important element in forensics analysis • 3 months of raw data on each node • Will soon be centralized • Compressed with bzip2 -9 • Parsed, summarized and stored centrally for statistical purposes Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  11. Conclusion • When does it work well ? • Repeated intruder activity • When there is a new intrusion pattern we quickly add a new scan pattern • Intruder doesn’t know our infrastructure • What are the limits ? • First time when there is a new way to break in we do not know about • Intruder discovers our infrastructure (clusters) Vladimir.Bahyl@cern.ch, Autumn HEPiX 2003, Triumf, Vancouver, Canada

  12. Questions ? • Contact: Vladimir.Bahyl@cern.ch • Thank you !