Data leakage detection using dynamic data structure and classification techniques

Data leakage is a permanent problem in public and private institutions around the world; particularly, identifying the information leakage efficiently. In order to solve this problem, this paper poses an adaptable data structure based on human behavior using all the activities executed within the co...

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Autores:
Guevara Maldonado, César Byron
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1748
Acceso en línea:
https://hdl.handle.net/11323/1748
https://repositorio.cuc.edu.co/
Palabra clave:
Data Leakage
Data Structure
Decision Tree C4.5
UCS
Naive Bayes
Fuga de Información
Estructura de Datos
Árbol de decisión
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
id RCUC2_72b0c72c98181043cd3c5683cdea2141
oai_identifier_str oai:repositorio.cuc.edu.co:11323/1748
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Data leakage detection using dynamic data structure and classification techniques
dc.title.translated.eng.fl_str_mv Detección de fugas de información aplicando estructura de dinámica de datos y técnicas de clasificación
title Data leakage detection using dynamic data structure and classification techniques
spellingShingle Data leakage detection using dynamic data structure and classification techniques
Data Leakage
Data Structure
Decision Tree C4.5
UCS
Naive Bayes
Fuga de Información
Estructura de Datos
Árbol de decisión
title_short Data leakage detection using dynamic data structure and classification techniques
title_full Data leakage detection using dynamic data structure and classification techniques
title_fullStr Data leakage detection using dynamic data structure and classification techniques
title_full_unstemmed Data leakage detection using dynamic data structure and classification techniques
title_sort Data leakage detection using dynamic data structure and classification techniques
dc.creator.fl_str_mv Guevara Maldonado, César Byron
dc.contributor.author.spa.fl_str_mv Guevara Maldonado, César Byron
dc.subject.eng.fl_str_mv Data Leakage
Data Structure
Decision Tree C4.5
UCS
Naive Bayes
Fuga de Información
Estructura de Datos
Árbol de decisión
topic Data Leakage
Data Structure
Decision Tree C4.5
UCS
Naive Bayes
Fuga de Información
Estructura de Datos
Árbol de decisión
description Data leakage is a permanent problem in public and private institutions around the world; particularly, identifying the information leakage efficiently. In order to solve this problem, this paper poses an adaptable data structure based on human behavior using all the activities executed within the computer system. When applying this structure, the normal behavior is modeled for each user, so in this way, detects any abnormal behavior in real time. Moreover, this structure enables the application of several classification techniques such as decision trees (C4.5), UCS, and Naive Bayes, these techniques have proven efficient outcomes in intrusion detection. In the testing of this model, a scenario demonstrating the proposal’s effectiveness with real information from a government institution was designed so as to establish future lines of work.
publishDate 2015
dc.date.issued.none.fl_str_mv 2015-01-05
dc.date.accessioned.none.fl_str_mv 2018-11-23T16:12:54Z
dc.date.available.none.fl_str_mv 2018-11-23T16:12:54Z
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dc.identifier.citation.spa.fl_str_mv Guevara Maldonado, C. (2015). Detección de Fugas de Información Aplicando Estructura de Dinámica de Datos y Técnicas de Clasificación. INGE CUC, 11(1), 79-84. Recuperado a partir de https://revistascientificas.cuc.edu.co/ingecuc/article/view/382
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identifier_str_mv Guevara Maldonado, C. (2015). Detección de Fugas de Información Aplicando Estructura de Dinámica de Datos y Técnicas de Clasificación. INGE CUC, 11(1), 79-84. Recuperado a partir de https://revistascientificas.cuc.edu.co/ingecuc/article/view/382
0122-6517, 2382-4700 electrónico
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dc.relation.ispartofseries.spa.fl_str_mv INGE CUC; Vol. 11, Núm. 1 (2015)
dc.relation.ispartofjournal.spa.fl_str_mv INGE CUC
INGE CUC
dc.relation.references.spa.fl_str_mv [1] A. Kumar, A. Goyal, N. K. Chaudhary, and S. Sowmya Kamath, “Comparative evaluation of algorithms for effective data leakage detection,” in Information & Communication Technologies (ICT), IEEE Conference, 2013, pp. 177–182. DOI:10.1109/CICT.2013.6558085
[2] E. Summary, “Data Leakage Worldwide: Common Risks and Mistakes Employees Make,” Europe, pp. 1–8, 2008.
[3] InfoWatch Research Center, "Global Data Leakages & Insider Threats Report, 2012". Disponible en: http://tech-titan.com/infowatch/pdf/InfoWatch%20Global%20Data%20Leakages%20and%20Insider%20Threats%20Report%202012.pdf
[4] W. L. W. Lee, S. J. Stolfo, and K. W. Mok, “A data mining framework for building intrusion detection models,” IEEE Symp. Secur. Priv., vol. 00, no. c, pp. 120–132, 1999. DOI:10.1109/SECPRI.1999.766909
[5] C. Guevara, M. Santos and J. A. Martín, "Método para la Detección de Intrusos basado en la Sinergia de Técnicas de Inteligencia Artificial," in Proceedings of the IV Congreso Español de Informática CEDI 2013, pp. 963-972.
[6] NSL-KDD. Disponible en: http://nsl.cs.unb.ca/NSL-KDD/
[7] J. McHugh, “Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory,” ACM Transactions on Information and System Security, vol. 3. pp. 262–294, 2000. DOI:10.1145/382912.382923
[8] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, 2009. DOI:10.1109/CISDA.2009.5356528
[9] M. Møller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural networks, vol. 6, pp. 525–533, 1993. DOI:10.1016/S0893-6080(05)80056-5
[10] B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: Perceptron, Madaline, and backpropagation,” Proc. IEEE, vol. 78, no. 9, pp. 1415–1442, 1990. DOI:10.1109/5.58323
[11] J. Quinlan, C4.5: Programs for Machine Learning, 240th ed. Londres: Morgan Kaufmann, 1993.
[12] J. R. Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986. DOI:10.1023/A:1022643204877
[13] J. C. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines,” Adv. Kernel Methods Support Vector Learn., vol. 208, pp. 1–21, 1998.
[14] S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy, “Improvements to Platt’s SMO Algorithm for SVM Classifier Design,” Neural Computation, vol. 13, no. 3. pp. 637–649, 2001. DOI:10.1162/089976601300014493
[15] D. Heckerman, “Bayesian Networks for Data Mining,” Data Min. Knowl. Discov., vol. 119, no. 1, pp. 79–119, 1997. DOI:10.1023/A:1009730122752
[16] S. W. Wilson, “Classifier Fitness Based on Accuracy,” Evolutionary Computation, vol. 3, no. 2. pp. 149–175, 1995. DOI:10.1162/evco.1995.3.2.149
[17] T. G. Dietterich, “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms,” Neural Computation, vol. 10, no. 7. pp. 1895–1923, 1998. DOI:10.1162/089976698300017197
[18] E. Bernadó-Mansilla and J. M. Garrell-Guiu, “Accuracy-based learning classifier systems: models, analysis and applications to classification tasks,” Evol. Comput., vol. 11, no. 3, pp. 209–238, 2003. DOI:10.1162/106365603322365289
[19] P. Domingos and M. Pazzani, “On the Optimality of the Simple Bayesian Classifier under Zero-One Los,” Mach. Learn., vol. 29, no. 2–3, pp. 103–130, 1997.
[20] D. Pyle, Data Preparation for Data Mining, 1st ed., vol. 1. San Francisco: Morgan Kaufmann, 1999. DOI:10.1023/A:1007413511361
[21] M. Basu, “Complexity measures of supervised classification problems,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 289–300, 2002. DOI:10.1109/34.990132
[22] H. Brighton and C. Mellish, “Advances in instance selection for instance-based learning algorithms,” Data Mining and Knowledge Discovery, vol. 6, no. 2. pp. 153–172, 2002. DOI:10.1023/A:1014043630878
[23] F. Ceballos, L. E. Muñoz, and J. Moreno, “Selección de perceptrones multicapa usando aprendizaje bayesiano,” Sci. Tech., no. 49, pp. 110–115, 2011.
[24] L. Rokach, “Ensemble-based classifiers,” Artif. Intell. Rev., vol. 33, no. 1–2, pp. 1–39, 2010. DOI:10.1007/s10462-009-9124-7
[25] H. Liu and R. Setiono, “Feature Selection and Classification: A Probabilistic Wrapper Approach,” in Proceedings of the 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1996, pp. 419–424.
[26] J. Alcala-Fdez, L. Sanchez, S. Garcia, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernandez, and F. Herrera, “KEEL: a software tool to assess evolutionary algorithms for data mining problems,” Soft Comput., vol. 13, no. 3, pp. 307–318, 2009. Disponible: //www.keel.es/
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spelling Guevara Maldonado, César Byron2018-11-23T16:12:54Z2018-11-23T16:12:54Z2015-01-05Guevara Maldonado, C. (2015). Detección de Fugas de Información Aplicando Estructura de Dinámica de Datos y Técnicas de Clasificación. INGE CUC, 11(1), 79-84. Recuperado a partir de https://revistascientificas.cuc.edu.co/ingecuc/article/view/3820122-6517, 2382-4700 electrónicohttps://hdl.handle.net/11323/17482382-4700Corporación Universidad de la Costa0122-6517REDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Data leakage is a permanent problem in public and private institutions around the world; particularly, identifying the information leakage efficiently. In order to solve this problem, this paper poses an adaptable data structure based on human behavior using all the activities executed within the computer system. When applying this structure, the normal behavior is modeled for each user, so in this way, detects any abnormal behavior in real time. Moreover, this structure enables the application of several classification techniques such as decision trees (C4.5), UCS, and Naive Bayes, these techniques have proven efficient outcomes in intrusion detection. In the testing of this model, a scenario demonstrating the proposal’s effectiveness with real information from a government institution was designed so as to establish future lines of work.La fuga de información es un problema que está presente en instituciones públicas y privadas alrededor del mundo. El principal problema que se presenta es identificar de forma eficiente el filtrado de la información. Para solucionar este problema en el presente trabajo desarrolla una estructura de datos adaptable al comportamiento humano, utilizando como base las actividades ejecutadas dentro del sistema informático. Al aplicar esta estructura se modela un comportamiento NORMAL de cada uno de los usuarios y de esta manera detecta cualquier comportamiento ANÓMALO en tiempo real. Además, permite la aplicación de varias técnicas de clasificación como los árboles de decisión (C4.5), UCS y Naive Bayes las cuales han demostrado un eficiente resultado en la detección de intrusiones. Para probar este modelo se ha diseñado un escenario que sirve para demostrar la validez de la propuesta con información real de una institución gubernamental y para acreditar líneas futuras de trabajo.Guevara Maldonado, César Byron-6079d61e-294f-441f-865f-72c7f1e30134-0application/pdfengCorporación Universidad de la CostaINGE CUC; Vol. 11, Núm. 1 (2015)INGE CUCINGE CUC[1] A. Kumar, A. Goyal, N. K. Chaudhary, and S. Sowmya Kamath, “Comparative evaluation of algorithms for effective data leakage detection,” in Information & Communication Technologies (ICT), IEEE Conference, 2013, pp. 177–182. DOI:10.1109/CICT.2013.6558085[2] E. Summary, “Data Leakage Worldwide: Common Risks and Mistakes Employees Make,” Europe, pp. 1–8, 2008.[3] InfoWatch Research Center, "Global Data Leakages & Insider Threats Report, 2012". Disponible en: http://tech-titan.com/infowatch/pdf/InfoWatch%20Global%20Data%20Leakages%20and%20Insider%20Threats%20Report%202012.pdf[4] W. L. W. Lee, S. J. Stolfo, and K. W. Mok, “A data mining framework for building intrusion detection models,” IEEE Symp. Secur. Priv., vol. 00, no. c, pp. 120–132, 1999. DOI:10.1109/SECPRI.1999.766909[5] C. Guevara, M. Santos and J. A. Martín, "Método para la Detección de Intrusos basado en la Sinergia de Técnicas de Inteligencia Artificial," in Proceedings of the IV Congreso Español de Informática CEDI 2013, pp. 963-972.[6] NSL-KDD. Disponible en: http://nsl.cs.unb.ca/NSL-KDD/[7] J. McHugh, “Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory,” ACM Transactions on Information and System Security, vol. 3. pp. 262–294, 2000. DOI:10.1145/382912.382923[8] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, 2009. DOI:10.1109/CISDA.2009.5356528[9] M. Møller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural networks, vol. 6, pp. 525–533, 1993. DOI:10.1016/S0893-6080(05)80056-5[10] B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: Perceptron, Madaline, and backpropagation,” Proc. IEEE, vol. 78, no. 9, pp. 1415–1442, 1990. DOI:10.1109/5.58323[11] J. Quinlan, C4.5: Programs for Machine Learning, 240th ed. Londres: Morgan Kaufmann, 1993.[12] J. R. Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986. DOI:10.1023/A:1022643204877[13] J. C. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines,” Adv. Kernel Methods Support Vector Learn., vol. 208, pp. 1–21, 1998.[14] S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy, “Improvements to Platt’s SMO Algorithm for SVM Classifier Design,” Neural Computation, vol. 13, no. 3. pp. 637–649, 2001. DOI:10.1162/089976601300014493[15] D. Heckerman, “Bayesian Networks for Data Mining,” Data Min. Knowl. Discov., vol. 119, no. 1, pp. 79–119, 1997. DOI:10.1023/A:1009730122752[16] S. W. Wilson, “Classifier Fitness Based on Accuracy,” Evolutionary Computation, vol. 3, no. 2. pp. 149–175, 1995. DOI:10.1162/evco.1995.3.2.149[17] T. G. Dietterich, “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms,” Neural Computation, vol. 10, no. 7. pp. 1895–1923, 1998. DOI:10.1162/089976698300017197[18] E. Bernadó-Mansilla and J. M. Garrell-Guiu, “Accuracy-based learning classifier systems: models, analysis and applications to classification tasks,” Evol. Comput., vol. 11, no. 3, pp. 209–238, 2003. DOI:10.1162/106365603322365289[19] P. Domingos and M. Pazzani, “On the Optimality of the Simple Bayesian Classifier under Zero-One Los,” Mach. Learn., vol. 29, no. 2–3, pp. 103–130, 1997.[20] D. Pyle, Data Preparation for Data Mining, 1st ed., vol. 1. San Francisco: Morgan Kaufmann, 1999. DOI:10.1023/A:1007413511361[21] M. Basu, “Complexity measures of supervised classification problems,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 289–300, 2002. DOI:10.1109/34.990132[22] H. Brighton and C. Mellish, “Advances in instance selection for instance-based learning algorithms,” Data Mining and Knowledge Discovery, vol. 6, no. 2. pp. 153–172, 2002. DOI:10.1023/A:1014043630878[23] F. Ceballos, L. E. Muñoz, and J. Moreno, “Selección de perceptrones multicapa usando aprendizaje bayesiano,” Sci. Tech., no. 49, pp. 110–115, 2011.[24] L. Rokach, “Ensemble-based classifiers,” Artif. Intell. Rev., vol. 33, no. 1–2, pp. 1–39, 2010. DOI:10.1007/s10462-009-9124-7[25] H. Liu and R. Setiono, “Feature Selection and Classification: A Probabilistic Wrapper Approach,” in Proceedings of the 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1996, pp. 419–424.[26] J. Alcala-Fdez, L. Sanchez, S. Garcia, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernandez, and F. Herrera, “KEEL: a software tool to assess evolutionary algorithms for data mining problems,” Soft Comput., vol. 13, no. 3, pp. 307–318, 2009. Disponible: //www.keel.es/INGE CUCINGE CUChttps://revistascientificas.cuc.edu.co/ingecuc/article/view/382Data LeakageData StructureDecision Tree C4.5UCSNaive BayesFuga de InformaciónEstructura de DatosÁrbol de decisiónData leakage detection using dynamic data structure and classification techniquesDetección de fugas de información aplicando estructura de dinámica de datos y técnicas de clasificaciónArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationORIGINALData Leakage Detection Using Dynamic Data.pdfData Leakage Detection Using Dynamic Data.pdfapplication/pdf855010https://repositorio.cuc.edu.co/bitstreams/44103467-affb-44ba-a30f-b99384255727/downloaded1467eb5416ab7539cd1537ce8e4a49MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/e3e45a86-85a0-4901-9a2c-c1238c24b6d5/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILData Leakage Detection Using Dynamic Data.pdf.jpgData Leakage Detection Using Dynamic Data.pdf.jpgimage/jpeg50846https://repositorio.cuc.edu.co/bitstreams/9b74c442-94e9-4160-b654-b99500345861/download96112158a042d5b37d02d97130948ef3MD54TEXTData Leakage Detection Using Dynamic Data.pdf.txtData Leakage Detection Using Dynamic Data.pdf.txttext/plain25571https://repositorio.cuc.edu.co/bitstreams/01ebc00e-4988-4076-8473-c47bace15559/download68206de057be92fc9ff171392604c1fbMD5511323/1748oai:repositorio.cuc.edu.co:11323/17482024-09-17 14:24:03.573open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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