The DOI matches its Crossref record.
Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm
Open accessAbstract
Intrusions pose a serious securing risk in a network environment. Network intrusion detection system aims to identify attacks or malicious activity in a network with a high detection rate while maintaining a low false alarm rate. Anomaly detection systems (ADS) monitor the behaviour of a system and flag significant deviations from the normal activity as anomalies. In this paper, we propose an anomaly detection method using “K-Means + C4.5”, a method to cascade k-Means clustering and the C4.5 decision tree methods for classifying anomalous and normal activities in a computer network. The k-Means clustering method is first used to partition the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build decision trees using C4.5 decision tree algorithm. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final conclusion we exploit the results derived from the decision tree on each cluster.
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
Cites
No references could be resolved yet.
Research works printed but not linked: 25
- Brause, 1999, Neural data mining for credit card fraud detection, In Proceedings of IEEE International Conference on Tools with Artificial Intelligence, 103, 10.1109/TAI.1999.809773 · DOI · Paper page
- Das, K. and Schneider, J., “Detecting anomalous records in categorical datasets,” In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining 2007. ACM Press. · DOI · Paper page
- Diehl, C. and Hampshire, J., “Real-time object classīcation and novelty detection for collaborative video surveillance”, In Proceedings of IEEE International Joint Conference on Neural Networks, 2002 IEEE, Honolulu, HI · DOI
- Duda, 2000, Pattern Classification (2nd Edition), Wiley–Interscience
- Fan, W., Miller, M., Stolfo, S.J., Lee, W., and Chan, P.K., “Using artificial anomalies to detect unknown and known network intrusions”, In Proceedings of the 2001 IEEE International Conference on Data Mining. IEEE Computer Society, 123-130.
- He, 2004, A frequent pattern discovery method for outlier detection”, 726
- Helmer, 1998, Intelligent agents for intrusion detection”, In Proceedings of IEEE Information Technology Conference, 121
- Hu, 2003, Robust anomaly detection using support vector machines, In Proceedings of the International Conference on Machine Learning Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 282
- Janakiram, 2006, Outlier detection in wireless sensor networks using bayesian belief networks”, In First International Conference on Communication System Software and Middleware, p.1, 10.1109/COMSWA.2006.1665221 · DOI · Paper page
- Lee, 1997, Learning patterns from Unix process execution traces for intrusion detection”, In Proceedings of the AAAI 97 workshop on AI methods in Fraud and risk management
- Lee, W. and Stolfo, S., “Data mining approaches for intrusion detection”, In Proceedings of the 7th USENIX Security Symposium, 1998, San Antonio, TX.
- Lee, 2000, Adaptive intrusion detection: A data mining approach, Artificial Intelligence Review, 14, 533, 10.1023/A:1006624031083 · DOI · Paper page
- Mahoney, M.V. and Chan, P.K., “Learning nonstationary models of normal network traffic for detecting novel attacks,” In Proceedings of the 8th ACM SIGKDD international conference on Knowledge discovery and data mining, 2002, ACM Press, pp. 376-385. · DOI
- Mahoney, M.V. and Chan, P.K., “Learning rules for anomaly detection of hostile network traffic”’ In Proceedings of the 3rd IEEE International Conference on Data Mining, 2003, IEEE Computer Society, pp. 601. · DOI · Paper page
- Mingming, 2000, Probabilistic networks with undirected links for anomaly detection”, In Proceedings of IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop, 175
- Mitchell, 1997, Machine Learning. McGraw-Hill
- Mukkamala, 2002, Intrusion detection: support vector machines and neural networks, In Proceedings of the IEEE international joint conference on neural networks, Honolulu USA;, 1702
- Qin, M. and Hwang, K., “Frequent episode rules for internet anomaly detection,” In Proceedings of the 3rd IEEE International Symposium on Network Computing and Applications, 2004, IEEE Computer Society. · DOI
- Ratsch, 2002, Constructing boosting algorithms from svms: An application to one-class classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 9, 1184, 10.1109/TPAMI.2002.1033211 · DOI · Paper page
- Roth, V., “Outlier detection with one-class kernel fisher discriminants”, In NIPS 2004.
- Roth, 2006, Kernel fisher discriminants for outlier detection”, Neural Computation 18, 4, 942, 10.1162/neco.2006.18.4.942 · DOI
- Song, 2002, Robust support vector machine with bullet hole image classification”, IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and Reviews, 32, 4
- Stefano, 2000, To reject or not to reject: that is the question–an answer in case of neural classifiers”, IEEE Transactions on Systems, Management and Cybernetics 30, 1, 84
- Tan, 2005, Introduction to Data Mining”, Addison-Wesley
- KDD Cup 1999 Data, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
Cited by
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
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
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
Real-time big data processing for anomaly detection: A survey
Ariyaluran Habeeb, R. A., Nasaruddin, F., Gani, A., Targio Hashem, I. A., Ahmed, E., & Imran, M. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.006 · Cited by 487
Machine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 470
Metadata
- Authors
- Muniyandi, A. P., Rajeswari, R., & Rajaram, R.
- Venue
- Procedia Engineering
- Publisher
- Elsevier BV
- Published
- 2012
- Volume
- 30
- Pages
- 174-182
- ISSN
- 1877-7058 (ISSN-L)
- OpenAlex
- W1976331960
- Cited by
- 251
- References
- 0
- FWCI
- 8.62
- Type
- conference paper
- Language
- en
- Open access
- diamond
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- creativecommons.org/licenses/by-nc-nd/3.0/ (vor, 562 d)
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: Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm. https://app.cochat.ai/papers/10.1016/j.proeng.2012.01.849
- The paper
- Muniyandi, A. P., Rajeswari, R., & Rajaram, R. (2012). Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm. Procedia Engineering. https://doi.org/10.1016/j.proeng.2012.01.849
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.
