The DOI matches its Crossref record.
A sense of self for Unix processes
Abstract
A method for anomaly detection is introduced in which "normal" is defined by short-range correlations in a process' system calls. Initial experiments suggest that the definition is stable during normal behaviour for standard UNIX programs. Further; it is able to detect several common intrusions involving sendmail and 1pr. This work is part of a research program aimed at building computer security systems that incorporate the mechanisms and algorithms used by natural immune systems.
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.
Integrity signals
Registry
Cites
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.
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
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
HOLMES: Real-Time APT Detection through Correlation of Suspicious Information Flows
Milajerdi, S. M., Gjomemo, R., Eshete, B., Sekar, R., & Venkatakrishnan, V. N. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00026 · Cited by 573
UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
Han, X., Pasquier, T., Bates, A., Mickens, J., & Seltzer, M. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24046 · Cited by 349
A defense-centric taxonomy based on attack manifestations
Killourhy, K. S., Maxion, R. A., & Tan, K. M. C. (2004) · International Conference on Dependable Systems and Networks, 2004 · doi:10.1109/dsn.2004.1311881 · Cited by 64
Landauer, M., Skopik, F., & Wurzenberger, M. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3660768 · Cited by 61
Metadata
- Authors
- Forrest, S., Hofmeyr, S. A., Somayaji, A., & Longstaff, T. A.
- Venue
- Proceedings 1996 IEEE Symposium on Security and Privacy
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-12-23
- Pages
- 120-128
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W3136767761
- Cited by
- 874
- References
- 0
- FWCI
- 74.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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: A sense of self for Unix processes. https://app.cochat.ai/papers/10.1109/secpri.1996.502675
- The paper
- Forrest, S., Hofmeyr, S. A., Somayaji, A., & Longstaff, T. A. (2002). A sense of self for Unix processes. Proceedings 1996 IEEE Symposium on Security and Privacy. https://doi.org/10.1109/secpri.1996.502675
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.
