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Automated Worm Fingerprinting. Authors: Sumeet Singh, Cristian Estan, George Varghese and Stefan Savage Publish: OSDI'04. Presenter: YanYan Wang. Introduction. Recent large scale internet worm post profound threat. Traditional detection methods are usually expensive and slow.

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automated worm fingerprinting

Automated Worm Fingerprinting

Authors: Sumeet Singh, Cristian Estan, George Varghese and Stefan Savage

Publish: OSDI'04.

Presenter: YanYan Wang

introduction
Introduction
  • Recent large scale internet worm post profound threat.
  • Traditional detection methods are usually expensive and slow.
  • This paper investigate “Early bird” method that automatically detect and contain new worms on the network using precise signature.
existing detecting techniques
Existing Detecting Techniques
  • Scan detection
    • Example: code red.
    • Network telescope: passive network monitors that observe large ranges of unused, yet routable, address space.
    • Assumption: worms select target victims at random
    • Limitations: not suited to non-random spreading worms
existing detecting techniques1
Existing Detecting Techniques
  • Honeypots
    • Monitoring idel hosts with untreated vulnerabilities
    • Limitations: requires significant amount of slow manual analysis, depend on the honeypot being quickly infected
existing detecting techniques2
Existing Detecting Techniques
  • Behavioral techniques at end hosts
    • Dynamically analyze the patterns of system calls for anomalous activity.
    • Limitations: expensive, only detect attack against a single host.
characterization
Characterization
  • Priori vulnerability signatures: match known exploitable vulnerabilities in deployed software.
  • Automation for signature extraction: extracts the infected decoy programs in a controlled environment and identify invariant code strings.
  • Autograph: (early bird)
containment
Containment
  • To slow or stop the spread of an active worm
    • Host quarantine: preventing an infect host from communicating with other hosts
    • String matching: matches network traffic against particular strings, or signatures
    • Connection throttling: limit rate of all outgoing connection made by a machine, slow but not stop
worm behavior
Worm Behavior
  • Content invariance
    • Program is identical across every host it infects, though some has limited polymorphism
    • Content prevalence: content not prevalent is not useful for constructing signatures
    • Address dispersion: the no. of infected hosts will grow over time
finding worm signature content sifting
Finding Worm Signature: Content Sifting
  • For each network:
    • Extract content and process substring
    • Index each substring into a prevalence table
    • Each table entry includes IP addresses
    • Sort the table
finding worm signature content sifting1
Finding Worm Signature: Content Sifting
  • Huge memory consumption: Multi-stage filters
finding worm signature content sifting2
Finding Worm Signature: Content Sifting
  • Address dispersion: trade precision for dramatic reductions in memory requirements
    • Example: For example, to count up to 64 sources using 32 bits, one might hash sources into a space from 0 to 63 yet only set bits for values that hash between 0 and 31 . thus ignoring half of the sources.
finding worm signature content sifting3
Finding Worm Signature: Content Sifting
  • Payload string requires significant processing: value sampling
    • select only those substrings for which the fingerprint matches a certain pattern.
    • Example: if f is the fraction of the tracked substrings (e.g. f = 1=64 if we track the substrings whose Rabin fingerprint ends on 6 0s), then the probability of detecting a worm with a signature of length x is
finding worm signature content sifting4
Finding Worm Signature: Content Sifting
  • If = 1=64 and = 40, the probability of tracking a worm with a signature of 100 bytes is 55%, but for a worm with a signature of 200 bytes it increases to 92%, and for 400 bytes to 99.64%.
early bird
Early Bird
  • As each packet arrives, its content (or substrings of its content) is hashed and appended with the protocol identifier and destination port to produce a content hash code.
    • 32 bit cyclic redundancy check (CRC)
    • 40 byte rabin fingerprints for substring hashses
early bird1
Early Bird
  • If the content hash is not found in the dispersion table, it is indexed into the content prevalence table.
    • 4 independent hash functions creat indexes into 4 counter arrays.
prototype system early bird
Prototype System : Early Bird
  • Sensor: sifts through traffic on configurable address space “zones” of responsibility and reports anomalous signature.
  • Aggregator: coordinated real-time updates from the sensors, coalesces related signatures, activates any network-level or host level blosing services and is responsible for administrative reporting and control.
  • Single threaded, excute at user-level, and captures packets using libpcap library.
what s the paper s contribution
What’s the paper’s contribution?
  • A combination of existing and novel algorithms for content sifting
  • Low memory and CPU requirements
what s the paper s weakness
What’s the paper’s weakness?
  • Depend on invariant content
    • Attackers can design variant content for worms
  • Attackers can evade by creating metamorphic worms and traditional IDS evasion techniques
  • Assume max growing time
  • Automated containment can be used trigger a worm defense by attackers.
how to improve the paper
How to improve the paper?
  • Hybrid pattern matching: separate non code string from potential exploits
  • Investigate traffic normalization
  • Maintain triggering date across multiple time scale
  • Develop efficient mechanisms for comparing signature with existing traffic corpus