The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A clustering-based method for unsupervised intrusion detections
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In the citing paper
“Jiang et al. [220] challenge this notion by showcasing that the size of the cluster is not sufficient to detect anomalies and has to be coupled with the distance of the cluster from other clusters, to increase accuracy of detection.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [220]
In the citing paper
“The KDD dataset is used for evaluation and shows that IMM outperforms three related works [131, 139, 363] in terms of detection rate and FP.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [220]
Citations
Cites
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925
Denning, D. E. (1987) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1987.232894 · Cited by 3,608
Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
Discovering cluster-based local outliers
He, Z., Xu, X., & Deng, S. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00003-5 · Cited by 1,060
A framework for constructing features and models for intrusion detection systems
Lee, W., & Stolfo, S. J. (2000) · ACM Transactions on Information and System Security · doi:10.1145/382912.382914 · Cited by 986
Intrusion detection with unlabeled data using clustering
Portnoy, L. (2000) · doi:10.7916/d8mp5904 · Cited by 759
Outlier Detection Using Replicator Neural Networks
Hawkins, S., He, H., Williams, G., & Baxter, R. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_17 · Cited by 750
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002) · Advances in Information Security · doi:10.1007/978-1-4615-0953-0_4 · Cited by 683
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
Eskin, E., Arnold, A. O., Prerau, M. J., Portnoy, L., & Stolfo, S. J. (2002) · doi:10.7916/d8d50tqt · Cited by 607
Research works printed but not linked: 1
- KDD data set, 1999. URL: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
Cited by
Sorted by citations
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Network Anomaly Detection: Methods, Systems and Tools
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.052213.00046 · Cited by 1,358
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Intrusion detection by machine learning: A review
Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., & Lin, W.-Y. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.029 · Cited by 969
AdaBoost-Based Algorithm for Network Intrusion Detection
Hu, W., Hu, W., & Maybank, S. J. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.914695 · Cited by 395
Metadata
- Authors
- Jiang, S., Song, X., Wang, H., Han, J.-J., & Li, Q.-H.
- Affiliations
- Guangdong University of Foreign Studies (CN)
- Wuhan Technical College of Communications (CN)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Jiang, ShengYi
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2006-05
- Volume
- 27
- Issue
- 7
- Pages
- 802-810
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2045012776
- Cited by
- 164
- References
- 19
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 15 · 2021: 8 · 2020: 8 · 2019: 10 · 2018: 10 · 2017: 10 · 2016: 11 · 2015: 14 · 2014: 8 · 2013: 12 · 2012: 10
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- outlier factor, network security, unknown attack detection, linear time complexity
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