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A Taxonomy of Computer Worms Nicholas Weaver, Vern Paxson, Stuart Staniford, and Robert Cunningham ACM WORM 2003 Speaker: Chang Huan Wu 2008/8/8.

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Presentation on theme: "A Taxonomy of Computer Worms Nicholas Weaver, Vern Paxson, Stuart Staniford, and Robert Cunningham ACM WORM 2003 Speaker: Chang Huan Wu 2008/8/8."— Presentation transcript:

1 A Taxonomy of Computer Worms Nicholas Weaver, Vern Paxson, Stuart Staniford, and Robert Cunningham ACM WORM 2003 Speaker: Chang Huan Wu 2008/8/8

2 2 Outline Introduction Classification of Worms – Target Discovery – Carrier – Activation – Payloads – Attackers Conclusions

3 3 Introduction What is a computer worm? – A program that propagates using vulnerabilities in software/application – Self-propagating (distinct from a virus) – Self-replicating In order to understand the worm threat, it is necessary to understand the various types of worms, payloads, and attackers

4 4 Target Discovery (1/3) Scanning – Sequential & Random – Optimization Preference for local addresses: Same OS and applications in a sub-network Permutation scanning: Utilize distributed coordination to more effectively scan Bandwidth-limited scanning: Do not wait for response – Anomalous from normal Internet traffic

5 5 Target Discovery (2/3) Pre-generated Target Lists – Attacker made a target list in advance Externally Generated Target Lists – Metaservers keep a list of all the servers which are currently active (Ex. Online game) Internal Target Lists – Victim’s applications contain information about other hosts

6 6 Target Discovery (3/3) Passive – Wait for potential victims to contact the worm (Ex. Un-patched browser) – Rely on user behavior to discover new targets Contagion worms rely on normal communication to discover new victims – No anomalous traffic patterns during target discovery

7 7 Carrier (1/2) Self-Carried – Transmits itself as part of the infection process Second Channel – Require a secondary communication channel to complete the infection (Ex. Blaster: exploit uses RPC, download the worm body by TFTP)

8 8 Carrier (2/2) Embedded – Sends itself as part of a normal communication channel, either appending to or replacing normal messages – Usually used by passive worms – Relatively stealthy

9 9 Activation (1/3) Human Activation – Convince a local user to execute the worm – The slowest activation approach Human Activity-Based Activation – Activated when the user performs some activity not normally related to a worm (Ex. resetting the machine, logging in)

10 10 Activation (2/3) Scheduled Process Activation – Unauthorized auto-updater programs – Ex. Use DNS redirection attack to serve a file to the desktop system to infect the target

11 11 Activation (3/3) Self Activation – Initiate their own execution by exploiting vulnerabilities in services that are always on and available – The fastest activation approach

12 12 Payloads (1/2) None/nonfunctional Internet Remote Control Spam-Relays Internet DoS Access for Sale

13 13 Payloads (2/2) Data Collection Data Damage Physical-world DoS – Use attached modems to dial emergency services Physical-world Damage – Reflashing BIOS …

14 14 Attackers (1/2) Experimental Curiosity – Continual tendency for various individuals to experiment with viruses and worms Pride and Power – A desire to acquire power, to show off their knowledge and ability to inflict harm on others Commercial Advantage – Profit by manipulating financial markets via a synthetic economic disaster

15 15 Attackers (2/2) Extortion and Criminal Gain – Credit-card information Random Protest – Disrupt networks and infrastructure Political Protest Terrorism Cyber Warfare

16 16 Conclusion Developed a taxonomy of worms – Target discovery, Carrier, Activation, Payloads, Attackers – The carrier, activation, and payload are independent of each other, and describe the worm itself – Sometimes the easiest way to defend against a worm is to remove the motivation for writing a worm in the first place

17 17 Comments Classify worms in many dimensions Different mechanism of Target Discovery / Carrier / Activation generate different traffic behaviors


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