Manuel Costa, Jon Crowcroft, Miguel Castro, Antony Rowstron, Lidong Zhou, Lintao Zhang, and Paul Barham
Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level.We propose Vigilante, a new end-to-end architecture to contain worms automatically that addresses these limitations.
In Vigilante, hosts detect worms by instrumenting vulnerable programs to analyze infection attempts. We introduce dynamic data-flow analysis: a broad-coverage host-based algorithm that can detect unknown worms by tracking the flow of data from network messages and disallowing unsafe uses of this data. We also show how to integrate other host-based detection mechanisms into the Vigilante architecture. Upon detection, hosts generate self-certifying alerts (SCAs), a new type of security alert that can be inexpensively verified by any vulnerable host. Using SCAs, hosts can cooperate to contain an outbreak, without having to trust each other. Vigilante broadcasts SCAs over an overlay network that propagates alerts rapidly and resiliently. Hosts receiving an SCA protect themselves by generating filters with vulnerability condition slicing: an algorithm that performs dynamic analysis of the vulnerable program to identify control-flow conditions that lead to successful attacks. These filters block the worm attack and all its polymorphic mutations that follow the execution path identified by the SCA.
Our results show that Vigilante can contain fast-spreading worms that exploit unknown vulnerabilities, and that Vigilante’s filters introduce a negligible performance overhead. Vigilante does not require any changes to hardware, compilers, operating systems, or the source code of vulnerable programs; therefore, it can be used to protect current software binaries.
In ACM Transactions on Computer Systems