{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T05:45:25Z","timestamp":1773467125037,"version":"3.50.1"},"publisher-location":"New York, New York, USA","reference-count":30,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The Vietnam National Foundation for Science and Technology Development (NAFOSTED)","award":["102.01-2014.09"],"award-info":[{"award-number":["102.01-2014.09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1145\/2676585.2676618","type":"proceedings-article","created":{"date-parts":[[2015,1,16]],"date-time":"2015-01-16T19:18:59Z","timestamp":1421435939000},"page":"286-291","source":"Crossref","is-referenced-by-count":11,"title":["Generating artificial attack data for intrusion detection using machine learning"],"prefix":"10.1145","author":[{"given":"Truong Son","family":"Pham","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quang Uy","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan Hoai","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/2676585.2676618-1","doi-asserted-by":"crossref","unstructured":"M. S. Abadeh, J. Habibi, Z. Barzegar, and M. Sergi. A parallel genetic local search algorithm for intrusion detection in computer networks.Eng. Appl. of AI, 20(8): 1058--1069, 2007.","DOI":"10.1016\/j.engappai.2007.02.007"},{"key":"key-10.1145\/2676585.2676618-2","doi-asserted-by":"crossref","unstructured":"C. C. Aggarwal.Outlier Analysis. Springer, 2013.","DOI":"10.1007\/978-1-4614-6396-2"},{"key":"key-10.1145\/2676585.2676618-3","doi-asserted-by":"crossref","unstructured":"H. B. Barlow. Unsupervised learning.Neural Computation, 1: 295--311, 1989.","DOI":"10.1162\/neco.1989.1.3.295"},{"key":"key-10.1145\/2676585.2676618-4","doi-asserted-by":"crossref","unstructured":"F. Bergadano. Machine learning and the foundations of inductive inference.Minds and Machines, 3(1): 31--51, 1993.","DOI":"10.1007\/BF00974304"},{"key":"key-10.1145\/2676585.2676618-5","doi-asserted-by":"crossref","unstructured":"V. L. Cao, V. T. Hoang, and Q. U. Nguyen. A scheme for building a dataset for intrusion detection systems. Inthe 2013 Third World Congress on Information and Communication Technologies, pages 120--132, Hanoi-Vietnam, 2013. IEEE.","DOI":"10.1109\/WICT.2013.7113149"},{"key":"key-10.1145\/2676585.2676618-6","doi-asserted-by":"crossref","unstructured":"W.-H. Chen, S.-H. Hsu, and H.-P. Shen. Application of SVM and ANN for intrusion detection.Computers &#38; OR, 32: 2617--2634, 2005.","DOI":"10.1016\/j.cor.2004.03.019"},{"key":"key-10.1145\/2676585.2676618-7","unstructured":"Y. Chen, A. Abraham, and B. Y. 0001. Hybrid flexible neural-tree-based intrusion detection systems.Int. J. Intell. Syst, 22(4): 337--352, 2007."},{"key":"key-10.1145\/2676585.2676618-8","unstructured":"N. Cristianini and J. Shawe-Taylor.An introduction to Support Vector Machines. Cambridge University Press, Mar. 2000."},{"key":"key-10.1145\/2676585.2676618-9","doi-asserted-by":"crossref","unstructured":"S. Das. Elements of artificial neural networks.IEEE Transactions on Neural Networks, 9(1): 234--235, Jan. 1998.","DOI":"10.1109\/TNN.1998.655048"},{"key":"key-10.1145\/2676585.2676618-10","doi-asserted-by":"crossref","unstructured":"D. E. Denning. An intrusion-detection model.IEEE Transactions on Software Engineering, 13(2): 222--232, Feb. 1987.","DOI":"10.1109\/TSE.1987.232894"},{"key":"key-10.1145\/2676585.2676618-11","doi-asserted-by":"crossref","unstructured":"W. Fan, M. Miller, S. Stolfo, W. Lee, and P. Chan. Using artificial anomalies to detect unknown and known network intrusions. InProceedings of ICDM01, pages 123--248, 2001.","DOI":"10.1109\/ICDM.2001.989509"},{"key":"key-10.1145\/2676585.2676618-12","unstructured":"S. Garcia and F. Herrera. Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy.Evolutionary Computation, 17(3): 275--306, 2009."},{"key":"key-10.1145\/2676585.2676618-13","doi-asserted-by":"crossref","unstructured":"G. Giacinto, R. Perdisci, M. D. Rio, and F. Roli. Intrusion detection in computer networks by a modular ensemble of one-class classifiers.Information Fusion, 9(1): 69--82, 2008.","DOI":"10.1016\/j.inffus.2006.10.002"},{"key":"key-10.1145\/2676585.2676618-14","doi-asserted-by":"crossref","unstructured":"R. Givan, S. Leach, and T. Dean. Bounded-parameter Markov decision processes.Artificial Intelligence, 122(1-2): 71--109, 2000.","DOI":"10.1016\/S0004-3702(00)00047-3"},{"key":"key-10.1145\/2676585.2676618-15","doi-asserted-by":"crossref","unstructured":"D. Heckerman. Tutorial on learning in bayesian networks. Technical Report MSR-TR-95-06, Microsoft, 1995.","DOI":"10.1016\/B978-1-55860-377-6.50079-7"},{"key":"key-10.1145\/2676585.2676618-16","doi-asserted-by":"crossref","unstructured":"N. Intrator. On the combination of supervised and unsupervised learning.Physica A, pages 655--661, 1993.","DOI":"10.1016\/0378-4371(93)90572-L"},{"key":"key-10.1145\/2676585.2676618-17","unstructured":"W. Lee, S. Stolfo, and K. Mok. A data mining framework for building intrusion detection models. InProceedings of the 1999 IEEE Symposium on Security and Privacy (SSP '99), pages 120--132, Washington - Brussels - Tokyo, 1999. IEEE."},{"key":"key-10.1145\/2676585.2676618-18","doi-asserted-by":"crossref","unstructured":"W. Lee and S. J. Stolfo. A framework for constructing features and models for intrusion detection systems.ACM Trans. Inf. Syst. Secur, 3(4): 227--261, 2000.","DOI":"10.1145\/382912.382914"},{"key":"key-10.1145\/2676585.2676618-19","doi-asserted-by":"crossref","unstructured":"Y. Li and L. Guo. An active learning based TCM-KNN algorithm for supervised network intrusion detection.Computers &#38; Security, 26(7-8): 459--467, 2007.","DOI":"10.1016\/j.cose.2007.10.002"},{"key":"key-10.1145\/2676585.2676618-20","doi-asserted-by":"crossref","unstructured":"Y. Liu, K. Chen, X. Liao, and W. Zhang. A genetic clustering method for intrusion detection.Pattern Recognition, 37(5): 927--942, 2004.","DOI":"10.1016\/j.patcog.2003.09.011"},{"key":"key-10.1145\/2676585.2676618-21","unstructured":"T. Mitchell.Machine Learning. McGraw-Hill, 1997."},{"key":"key-10.1145\/2676585.2676618-22","doi-asserted-by":"crossref","unstructured":"S. Mukkamala, A. H. Sung, and A. Abraham. Intrusion detection using an ensemble of intelligent paradigms.J. Network and Computer Applications, 28(2): 167--182, 2005.","DOI":"10.1016\/j.jnca.2004.01.003"},{"key":"key-10.1145\/2676585.2676618-23","doi-asserted-by":"crossref","unstructured":"Quinlan. Learning decision tree classifiers.CSURV: Computing Surveys, 28, 1996.","DOI":"10.1145\/234313.234346"},{"key":"key-10.1145\/2676585.2676618-24","unstructured":"J. R. Quinlan.C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993."},{"key":"key-10.1145\/2676585.2676618-25","doi-asserted-by":"crossref","unstructured":"K. Shafi and H. A. Abbass. Evaluation of an adaptive genetic-based signature extraction system for network intrusion detection.Pattern Anal. Appl, 16(4): 549--566, 2013.","DOI":"10.1007\/s10044-011-0255-5"},{"key":"key-10.1145\/2676585.2676618-26","unstructured":"C.-F. Tsai, Y.-F. Hsu, C.-Y. Lin, and W.-Y. Lin. Intrusion detection by machine learning: A review.Expert Systems with Applications, 36(10): 11994--12000, 2009."},{"key":"key-10.1145\/2676585.2676618-27","unstructured":"V. Vapnik.Statistical Learning Theory. Wiley, 1998."},{"key":"key-10.1145\/2676585.2676618-28","unstructured":"I. H. Witten and E. Frank.Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005."},{"key":"key-10.1145\/2676585.2676618-29","unstructured":"S. X. Wu and W. Banzhaf. The use of computational intelligence in intrusion detection systems: A review.Appl. Soft Comput, 10(1): 1--35, 2010."},{"key":"key-10.1145\/2676585.2676618-30","unstructured":"H. Zhang. The optimality of naive bayes.17th International FLAIRS conference, Miami Beach, May, pages 17--19, 2004."}],"event":{"name":"the Fifth Symposium","location":"Hanoi, Viet Nam","acronym":"SoICT '14","number":"5","start":{"date-parts":[[2014,12,4]]},"end":{"date-parts":[[2014,12,5]]}},"container-title":["Proceedings of the Fifth Symposium on Information and Communication Technology - SoICT '14"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2676585.2676618","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=2676618&amp;ftid=1529189&amp;dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:14:30Z","timestamp":1750277670000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=2676585.2676618"}},"subtitle":[],"proceedings-subject":"Information and Communication Technology","short-title":[],"issued":{"date-parts":[[2014]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1145\/2676585.2676618","relation":{},"subject":[],"published":{"date-parts":[[2014]]}}}