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Building ICAS with Hadoop and HBase

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Detecting unwanted attempts at accessing, manipulating or disabling ... Making alerts algorism to generate manifest report. Reducing redundancy. Merge relation ... – PowerPoint PPT presentation

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Title: Building ICAS with Hadoop and HBase


1
Building ICAS with Hadoop and HBase
2
Outline
  • Background Issues
  • Motivation Proposal
  • ICAS Architecture
  • ICAS Procedure
  • Experiment Results
  • Pros and Cons
  • Conclusions

3
IDS Introduction
  • Intrusion Detection System (IDS)
  • Detecting unwanted attempts at accessing,
    manipulating or disabling of computer systems
    through Internet.
  • Alert
  • Be produced when IDS detect something as
    malicious.

4
HIDS v.s. NIDS
  • Host IDS
  • Easy to control and maintain
  • Network IDS (NIDS)
  • Monitoring network traffic both incoming and
    outgoing.
  • Alerts are much more and more complex.

5
  • Host IDS is easily to maintain

6
Network IDS can only show many alerts!
7
Whats problem about Alerts of NIDS ?
  • Difficult to realize the overall accidents
  • Huge Data ? less Efficient
  • Ignoring the crucial information easily !!!
  • Got Nothing if the database were crash

8
Our Motivation
  • To resolve above problems come with huge amount
    of anomaly information generated by NIDS
  • So, we need
  • Reducing redundancy
  • Merge relation
  • Higher capability
  • Fault tolerance

9
Our IDEA - ICAS
  • ICAS, IDS Cloud Analysis System
  • Applying Cloud Computing technique
  • Higher capability
  • Fault tolerance
  • Making alerts algorism to generate manifest
    report
  • Reducing redundancy
  • Merge relation

10
System Architecture
ICAS Component Overview
11
System Architecture
Snort
  • SNORT is an open source network intrusion
    prevention and detection system
  • The most widely deployed intrusion detection

12
System Architecture
Why is Snort
  • The most popular (over 1m downloads and 200k
    registered users)
  • Open source network IDS
  • Support Windows and Linux
  • Light weight and easy to extend
  • High accuracy and performance

13
System Architecture
Hadoop
  • Apache Hadoop Core is a software platform that
    lets one easily write and run applications that
    process vast amounts of data.
  • Inspired by Google's MapReduce and Google File
    System (GFS) papers
  • Implements MapReduce and Hadoop Distributed File
    System (HDFS)
  • Operates ltkey, valuegt pairs

14
System Architecture
Why is Hadoop
  • The most popular open source Cloud platform
  • Support its API for developments
  • Scalable, economical, efficient, and reliable
  • Scaling Hadoop to 4000 nodes at Yahoo! (2008-09)
  • Hadoop Sorts a Petabyte in 16.25 Hours and a
    Terabyte in 62 Seconds (2009-05)

15
System Architecture
HBase
  • HBase is the Hadoop database
  • An open-source, distributed, column-oriented
    store modeled after the Google paper, BigTable

16
System Architecture
Why is HBase
  • The Hadoop database
  • Output can installed into HBase directly
  • Support its API for development

17
System Architecture
Four Components
  • Regular Parser
  • Parsing original snort log and transfer to HDFS
    (hadoop file system)
  • Analysis Procedure
  • Dispatch job if pool is not empty and insert the
    result into database
  • Data Mapper
  • ltkey, valuegt mapping
  • Data Reducer
  • ltkey1, value1valueNgt
  • ltkey2, value1valueNgt

18
Program Procedure
19
Alert Integration Procedure
20
Key - Values
The victim IP addresses
A unique ID used to identify attack method in
Snort rules
The time when the attack was launghed
TCP/IP protocol
Victim ports
The IP address where malicious one launghed attack
Attack was lunched from this port
21
Alert Merge Example
22
Experiment Environment
  • Machine ( 6 nodes)
  • CPU Intel quad-core, Memory 2G
  • OS Linux Ubuntu 8.04 server
  • Software
  • Hadoop core 0.16.4
  • Hbase 0.1.3
  • Java 6
  • Alerts Data Sets
  • MIT Lincoln Laboratory, Lincoln Lab Data Sets
  • Computer Security group at UCDavis, tcpdump file

23
Experimental Result
The Consuming Time of Each Number of Data Sets
24
Experimental Result
Throughput Data Overall
25
Pros Cons
  • Legible
  • Efficient
  • Scalable
  • Economical
  • Reliable
  • Non-realtime
  • Latency
  • Immature

26
Hadoop Development Issues
  • Absolute dependency of Hadoop Version ( Neither
    backward nor upward compatibility)
  • ICAS can work on Hadoop 0.16.4.
  • ICAS has 8 errors and 8 deprecations on Hadoop
    0.18.3
  • ICAS has 26 errors and 22 deprecations on hadoop
    0.20.0
  • A word-count sample code on hadoop 0.20 cant
    work for hadoop 0.18
  • HBases A version is only correspond to
    Hadoops A version (upper or lower not work)
  • Sample codes are hardly to find

27
Conclusions v.s. Future Works
  • ICAS supplies a efficient way to analyze and
    merge huge number of alerts based on cloud
    platform.
  • Support more types of IDS
  • Visualize the final results
  • Prepare more large-scale and complete experiment

28
Thank You ! Question ?
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