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Experiences with tools for network anomaly detection in the G

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Backbone network for National Research and Education Networks in Europe ... Evidence collection. NfSen. Connect. Communicate. Collaborate ... – PowerPoint PPT presentation

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Title: Experiences with tools for network anomaly detection in the G


1
Experiences with tools for network anomaly
detection in the GÉANT2 core
  • Maurizio Molina, DANTE
  • COST TMA tech. Seminar
  • Samos, 23rd Sep 2008

2
The GÉANT Network
  • DANTE operates GÉANT2
  • Backbone network for National Research and
    Education Networks in Europe
  • 30 NRENs, 2 global connectivity providers (Telia
    and GCrossing), peerings with other research
    networks (Abilene, Canarie, Clara, TEIN2, SINET)

3
The GÉANT Network (IP layer)
  • 20 Juniper routers
  • tenths of GBit/s of aggregated traffic
  • Main accesses and the backbone 10Gbit/s

Pls see www.dante.net
4
The Services
  • So. Just a big pipe? No!
  • Services
  • Dedicated L1-L2 circuits via multiple
    technologies
  • Performance Monitoring services (perfSONAR)
  • Support for federation of National AA
    Infrastructures (eduGAIN) and wireless roaming
    (eduROAM)
  • Security Service

NEW!
Very NEW!
5
The visionenhance NRENs security
  • NRENs have their ( - evolved) CERTs to deal
    with security
  • and DANTE can filter traffic on GÉANT upon NRENs
    request.
  • ! BUT !
  • Can we be more proactive to NREN CERTs exploiting
    the visibility of the GN2 core?

6
The vision (cont.)enhance NRENs security
  • Approach NetFlow ( Routing data) good
    processing tools
  • Netflow collected on all peering interfaces
  • 1 / 1,000 Sampling
  • 3k flows/s

7
Proof of concept Can we identify anomalies in
the core?
  • Anomalies are often hidden
  • Requirements
  • High detection rate
  • Low false positives
  • Anomaly classification
  • Evidence collection

NfSen
8
From volume to IP feature entropies
  • IP features entropies
  • Simple linear filter

9
Drilling down on peaks
  • IRC server in Slovenia, receiving a lot of 60
    bytes syn pkts on port 6667, mainly from a /16
    Subnetwork of an University in the Netherlands.
  • Likely a BotNet war?
  • -Concentration of DST IPs and DST ports
    receiving flows
  • -Dispersion of SRC IPs and SRC ports

10
Drilling down on peaks (cont.)
  • - Concentration of SRC and DST IPs and SRC ports
  • - Dispersion of DST ports
  • Portscan of host in CARNET, from 4 hosts, 29
    bytes packets

11
Open source tools
  • Results
  • anomalies are observable in the GÉANT2 core
  • Novel methodologies (IP Features entropy) for
    their classifications are applicable
  • Limits
  • NfSen does not fuse NetFlow and Routing data
  • Extensions would need to be run (and tuned) on
    all ingress/egress points
  • No support, no guaranteed development

12
Commercial tools
  • Test started Jun 08 (3 tools)
  • Tool 1
  • PCA, entropy
  • Tool 2
  • Large scale DDoS and Worm spread
  • Tool 3
  • Per host behaviour

13
Tool 1 (as a security tool)
  • Two main novel elements
  • Principal Component Analysis (PCA)
  • Both Volume and IP features Entropy anomaly
    detection
  • Address what makes anomaly detection a complex
    task
  • PCA single parameter to control detection
    sensitivity, even if anomalies are attributed to
    specific OD pairs
  • Entropy Detection of both low volume (scans) and
    high volume (DoS) anomalies

14
Demo.
  • . Or Screenshots.

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23
Tool 2
  • Well-established (and expensive!) solution for
    detecting large events
  • Originally based on large volume shifts only
  • Now enhanced to give alerts on fingerprints
    (e.g. communication with CC servers)
  • Shared by (part) of the user community (50 out of
    120)
  • No usage of routing data
  • though zones can be manually created via BGP
    prefixes lists
  • Traditional threshold based detection (although
    adaptive)

24
Tool 3
  • Per host behavioural analysis
  • rather complex scoring system to distinguish
    normal from abnormal behaviour. Proprietary
    algorithms
  • Doesnt use routing info
  • though zones can be manually created via BGP
    prefixes lists
  • Potentially attractive methodology
  • Concerns on scalability and accuracy with 1,000
    sampling

25
lessons learnt and directions for research
  • Manual validation is required to confirm/correct
    anomalies
  • More automatic intelligence to help this process
  • Fusion with other data sources (router logs?
    Honeynets?)
  • Detection space of 3 tools often disjoint
  • (Standard) anomaly injection
  • Operations need supported tools to support
    services
  • If choice is among published but not a tool or
    secret but supported and (claiming to) work gt
    risk to stick to those!
  • Fill the gap towards TOOLS!

26
Thank you!
  • maurizio.molina_at_dante.org.uk
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