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Mind map

U11.1 Intrusion detection

Craig Parker Network Security Masters Degree English

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U11.1 Intrusion detection

  1. Firewalls can only prevent attacks from external attack

    • Cant detect or prevent attacks
      • Do not react to attacks
  2. IDS combats insider threats

    • Insiders with access to resources
      • Easier to exploit from inside
        • Firewalls cant protect from the insiders
          • Manual processes for detecting intruders are too complex and resource heavy, esp on a large estate
  3. Automated system to monitor multiple hosts

    • for suspicious behaviour
      • reports on this behaviour
  4. Knowledge based or misuse detection IDS (Most common)

    • Most IDS look for attack signatures
      • specific patterns of NW traffic or activity in log files to indicate specific behaviour
        • IE: number of failed logins, certain types of TCP SYN packets, buffer overflow
    • Uses Known vulnerabilities, known attacks on systems
    • Only as good as the information in the database of signatures
      • New Vulnerabilities not in the DB can be exploited
        • Vendors need to keep up to dat with latest attacks and issue updates
          • large DB makes IDS slow
          • Gap between exploit being discovered and attack signature update for the DB
  5. Behaviour based IDS

    • Statistical anomaly detection or behaviour based detection is where statistical techniques are used to detect penetrations and attacks
      • Begin by establishing base line behaviour, what is normal
        • Then gather new data and measure the deviation
          • if threshold is exceeded, issue an alert
        • monitor number of failed logins
          • Raises the issues of false positives and false negatives where something falls within the boundary of normal behaviour
            • does also apply to knowledge based systems
            • Fine tuning required in order to get optimal setting, like biometrics
    • Not widely used

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Masters Degree Network Security Mind Map on U11.1 Intrusion detection, created by Craig Parker on 03/20/2014.
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Craig Parker
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