Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 670

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Polygraph: Automatically Generating Signatures for Polymorphic Worms

Open access
Newsome, J., Karp, B., Song, D.·2005·2005 IEEE Symposium on Security and Privacy (S&P'05)

Abstract

It is widely believed that content-signature-based intrusion detection systems (IDS) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/sp.2005.15

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 670

Metadata

Authors
Newsome, J., Karp, B., & Song, D.
Venue
2005 IEEE Symposium on Security and Privacy (S&P'05)
Publisher
IEEE
Published
2005-05-24
Pages
226-241
ISSN
1081-6011 (ISSN-L)
OpenAlex
W2137786570
Cited by
670
References
1
FWCI
44.27
Type
conference paper
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Polygraph: Automatically Generating Signatures for Polymorphic Worms. https://app.cochat.ai/papers/10.1109/sp.2005.15
The paper
Newsome, J., Karp, B., & Song, D. (2005). Polygraph: Automatically Generating Signatures for Polymorphic Worms. 2005 IEEE Symposium on Security and Privacy (S&P'05). https://doi.org/10.1109/sp.2005.15

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.