Reaching a Consensus on Access Detection by a Decision System
| dc.contributor.author | Santos Peñas, Matilde | |
| dc.contributor.author | Guevara Maldonado, César Byron | |
| dc.contributor.author | López López, María Victoria | |
| dc.contributor.author | Martín, José Antonio | |
| dc.date.accessioned | 2023-06-19T14:56:20Z | |
| dc.date.available | 2023-06-19T14:56:20Z | |
| dc.date.issued | 2014-12-02 | |
| dc.description.abstract | Classification techniques based on Artificial Intelligence are computational tools that have been applied to detection of intrusions (IDS) with encouraging results. They are able to solve problems related to information security in an efficient way. The intrusion detection implies the use of huge amount of information. For this reason heuristic methodologies have been proposed. In this paper, decision trees, Naive Bayes, and supervised classifying systems UCS, are combined to improve the performance of a classifier. In order to validate the system, a scenario based on real data of the NSL-KDD99 dataset is used. | |
| dc.description.department | Depto. de Arquitectura de Computadores y Automática | |
| dc.description.faculty | Fac. de Informática | |
| dc.description.refereed | TRUE | |
| dc.description.sponsorship | Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Government of the Republic of Ecuador | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/33210 | |
| dc.identifier.doi | 10.1109/PIC.2014.6972308 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/34872 | |
| dc.issue.number | 697230 | |
| dc.journal.title | PIC 2014 - Proceedings of 2014 IEEE International Conference on Progress in Informatics and Computing | |
| dc.language.iso | eng | |
| dc.page.final | 122 | |
| dc.page.initial | 119 | |
| dc.rights.accessRights | open access | |
| dc.subject.cdu | 004 | |
| dc.subject.keyword | Artificial intelligence | |
| dc.subject.keyword | Heuristic methodologies | |
| dc.subject.keyword | intrusiondDetection (IDS) | |
| dc.subject.keyword | Decision trees | |
| dc.subject.keyword | Supervised dlassifying system UCS | |
| dc.subject.keyword | Naive Bayes | |
| dc.subject.ucm | Informática (Informática) | |
| dc.subject.unesco | 1203.17 Informática | |
| dc.title | Reaching a Consensus on Access Detection by a Decision System | |
| dc.type | journal article | |
| dcterms.references | [1] H. Debar and J. Viinikka, "Introduction to Intrusion Detection and Security Information Management", in Foundations of Security Analysis and Design III FOSAD 2005. LNCS, 3655, pp. 207-236. Springer (2005). [2] R.G. Bace and P. Mell. "Intrusion detection systems. Gaithersburg", in U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001. [3] M. Esposito , C. Mazzariello, F. Oliviero, S.P. Romano, and C. Sansone “Evaluating pattern recognition techniques in intrusion detection systems.” in Proceedings of the 5th Interna-tional Workshop on Pattern Recognition in Information Systems (PRIS) 2005, May 2005, pp. 144 - 153. [4] W. Lee , S.J. Stolfo, and K. Mok, "Data Mining in work flow environments: Experiments in intrusion detection." in Proceedings of the 1999 Conference on Knowledge Dis-covery and Data Mining. [5] V. Jaiganesh, S. Mangayarkarasi and P. Sumathi, "Intrusion Detection Systems: A Survey and Analysis of Classification Techniques." vol, 2, 1629-1635. [6] A. Mitrokotsa and C. Dimitrakakis, "Intrusion detection in MANET using classification algorithms: The effects of cost and model selection.", Ad Hoc Networks, 11(1), 226-237. [7] C. Guevara, M. Santos, and J.A. Martín-H, "Identification of Computer Information System Intruders by Decision Trees and Artificial Neural Networks" in International Conference on Intelligent Systems and Knowledge Engineering ISKE 2013. [8] W. Wang, X. Zhang, S. Gombault, and S.J. Knapskog, "Attribute Normalization in Network Intrusion Detection", IEEE, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks. 978-0-7695-3908-9/09, pp-448-453. [9] S. Haykin, "Neural networks: a comprehensive foundation.", New York: Macmillan, 2004. [10] J.R. Quinlan, "Induction of Decision Trees. Machine Learning 1", (1986) 81-106. [11] C. X. Ling, Q. Yang, J. Wang, and S. Zhang, "Decision trees with minimal costs." in Proceedings of the twenty-first international conference on Machine learning, (2004, July), (p. 69). ACM. [12] S. Abe, "Support vector machines for pattern classification.", London: Springer, 2005. [13] E. Bernadó-Mansilla and J.M. Garrell., "Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks." in Evolutionary Computation 11:3 (2003) 209-238. [14] P. Domingos and M. Pazzani, "On the optimality of the simple Bayesian classifier under zero-one loss." in Machine Learning 29 (1997) 103-137. [15] "NSL-KDD data set for network-based intrusion detection systems.” Available: http://nsl.cs.unb.ca/NSL-KDD/, March 2014. [16] A.W. Moore and M.S. Lee, "Efficient Algorithms for Minimizing Cross Validation Error.", in Machine Learning: Proceedings of the Eleventh International Conference, Morgan Kaufmann, 1993. [17] M. Santos , J. A. Martín H, V. López and G. Botella, "Dyna-H: A heuristic planning reinforcement learning algorithm applied to roleplaying game strategy decision systems", Knowledge-Based Systems (2012), 32, 28-36. | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 99cac82a-8d31-45a5-bb8d-8248a4d6fe7f | |
| relation.isAuthorOfPublication | f806566f-1e28-4933-b145-c9531c1ded1c | |
| relation.isAuthorOfPublication.latestForDiscovery | 99cac82a-8d31-45a5-bb8d-8248a4d6fe7f |
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