Automated worm fingerprinting

TLDR

Network worms pose a growing threat due to unrestricted connectivity and software homogeneity, enabling rapid, widespread propagation that outpaces human response. The paper proposes an automated method to detect unknown worms and viruses by exploiting common exploit sequences and unique infection source patterns. The approach automatically generates precise signatures for filtering or moderating worm spread, implemented via scalable content‑sifting algorithms with low memory and CPU usage. In months of deployment at UCSD, the Earlybird prototype automatically detected and signed all active pathogens and several previously unknown worms and viruses, indicating that fully automated defenses may be practical against zero‑day epidemics.

Abstract

Network worms are a clear and growing threat to the security of today's Internet-connected hosts and networks. The combination of the Internet's unrestricted connectivity and widespread software homogeneity allows network pathogens to exploit tremendous parallelism in their propagation. In fact, modern worms can spread so quickly, and so widely, that no human-mediated reaction can hope to contain an outbreak. In this paper, we propose an automated approach for quickly detecting previously unknown worms and viruses based on two key behavioral characteristics - a common exploit sequence together with a range of unique sources generating infections and destinations being targeted. More importantly, our approach - called - automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. Using a combination of existing and novel algorithms we have developed a scalable content sifting implementation with low memory and CPU requirements. Over months of active use at UCSD, our Earlybird prototype system has automatically detected and generated signatures for all pathogens known to be active on our network as well as for several new worms and viruses which were unknown at the time our system identified them. Our initial experience suggests that, for a wide range of network pathogens, it may be practical to construct fully automated defenses - even against so-called zero-day epidemics.