The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learning rules for anomaly detection of hostile network traffic
Abstract
We introduce an algorithm called LERAD that learns rules for finding rare events in nominal time-series data with long range dependencies. We use LERAD to find anomalies in network packets and TCP sessions to detect novel intrusions. We evaluated LERAD on the 1999 DARPA/Lincoln Laboratory intrusion detection evaluation data set and on traffic collected in a university departmental server environment.
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Citations
Cites
Bro: a system for detecting network intruders in real-time
Paxson, V. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00112-7 · Cited by 2,636
Adaptive, model-based monitoring for cyber attack detection
Valdes, A., & Skinner, K. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-39945-3_6 · Cited by 309
Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Machine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
Effective Intrusion Detection System Using XGBoost
Dhaliwal, S. S., Nahid, A.-A., & Abbas, R. (2018) · Information · doi:10.3390/info9070149 · Cited by 503
A hybrid intrusion detection system design for computer network security
Aydın, M. A., Zaim, A. H., & Ceylan, K. G. (2009) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2008.12.005 · Cited by 288
Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks
de Campos Souza, P. V., Guimarães, A. J., Rezende, T. S., Silva Araujo, V. J., & Araujo, V. S. (2020) · AI · doi:10.3390/ai1010005 · Cited by 16
Metadata
- Authors
- Mahoney, M. V., & Chan, P. K.
- Venue
- Third IEEE International Conference on Data Mining
- Publisher
- IEEE
- Published
- 2003
- Pages
- 601-604
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2147191819
- Cited by
- 201
- References
- 2
- FWCI
- 5.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mahoney, M. V., & Chan, P. K. (2003). Learning rules for anomaly detection of hostile network traffic. Third IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2003.1250987
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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.
