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Security components of the CERN farm nodes

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2 CPUs / 1 GB RAM / 20 GB disk. 100 Mbit network. CERN RedHat Linux 7.3.3. 50-70 users ... kernel ptrcchk: uid=19201 tried ptrace on suid/sgid file /usr/bin/passwd ... – PowerPoint PPT presentation

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Title: Security components of the CERN farm nodes


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

2
Outline
  • Current state
  • Our typical system
  • Possible risks
  • Protection methods
  • Security related
  • Against denial of service
  • Conclusion

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

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

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

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

7
Netlog example
  • Incoming connections
  • Oct 14 214943 node kernel netlog info
    connect start TCP 172.17.35.9844073 lt-
    192.168.161.9052453 31440 31476 240 pmg_agent
  • Oct 14 214943 node kernel netlog info
    connect stop TCP 172.17.35.9844073 lt-
    192.168.161.9052453 31440 31476 240 pmg_agent
  • Outgoing connections
  • Oct 14 181849 node kernel netlog info
    connect start TCP 172.17.35.9834434 -gt
    192.168.11.1080 22163 23183 1648 wget
  • Oct 14 181852 node kernel netlog info
    connect stop TCP 172.17.35.9834434 -gt
    192.168.11.1080 22163 23183 1648 wget

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)

9
Scan for certain patterns
  • For example
  • IRC activity
  • IRC servers violate CERNs computing rules
  • SUID ptrace exploit attempts
  • 2003/07/23-131038 Uu ? node.cern.ch172.17.35.9
    8 kernel request_modulenet-pf-14
    waitpid(28284,...) failed, errno 512
  • 2002/01/31-014623 Uu ? node.cern.ch172.17.35.9
    8 kernel ptrcchk uid19201 tried ptrace on
    suid/sgid file /usr/bin/passwd
  • Generated by a proprietary kernel module
  • network sniffer
  • 2003/10/03-174454 Uu ? node.cern.ch172.17.35.9
    8 kernel device eth0 entered promiscuous mode
  • repeated login failures
  • etc.
  • This data can use used together with network IDS
    results

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

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 doesnt 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)

12
Questions ?
  • Contact
  • Vladimir.Bahyl_at_cern.ch
  • Thank you !
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