Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis
Open source intelligence (OSINT) is used to obtain and analyze information related to adversaries, so it can support risk assessments aimed to prevent damages against critical assets. This paper presents a research about different OSINT technologies and how these can be used to perform cyber intelli...
- Autores:
-
Hernandez Mediná, Martin Jose
Pinzón Hernández, Cristian Camilo
Díaz López, Daniel Orlando
Garcia Ruiz, Juan Carlos
Pinto Rico, Ricardo Andrés
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2018
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1459
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1459
https://doi.org/10.14483/2322939X.13504
https://revistas.udistrital.edu.co/index.php/vinculos/article/view/13504
- Palabra clave:
- OSINT
Ciberinteligencia (seguridad informática)
Seguridad informática
Cyberintelligence
Open source intelligence
Adversary profiling
Machine learning
Sentiment analysis
Data science
Análisis de sentimientos
Aprendizaje automático
Ciber inteligencia
Ciencia de datos
Inteligencia de fuentes abiertas
Perfilamiento de adversarios
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by/4.0/
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Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| dc.title.alternative.spa.fl_str_mv |
Inteligencia de fuentes abierta (OSINT) para operaciones de ciberseguridad. “Aplicación de OSINT en un contexto colombiano y análisis de sentimientos |
| title |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| spellingShingle |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis OSINT Ciberinteligencia (seguridad informática) Seguridad informática Cyberintelligence Open source intelligence Adversary profiling Machine learning Sentiment analysis Data science Análisis de sentimientos Aprendizaje automático Ciber inteligencia Ciencia de datos Inteligencia de fuentes abiertas Perfilamiento de adversarios |
| title_short |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| title_full |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| title_fullStr |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| title_full_unstemmed |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| title_sort |
Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis |
| dc.creator.fl_str_mv |
Hernandez Mediná, Martin Jose Pinzón Hernández, Cristian Camilo Díaz López, Daniel Orlando Garcia Ruiz, Juan Carlos Pinto Rico, Ricardo Andrés |
| dc.contributor.author.none.fl_str_mv |
Hernandez Mediná, Martin Jose Pinzón Hernández, Cristian Camilo Díaz López, Daniel Orlando Garcia Ruiz, Juan Carlos Pinto Rico, Ricardo Andrés |
| dc.contributor.researchgroup.spa.fl_str_mv |
CTG-Informática |
| dc.subject.armarc.eng.fl_str_mv |
OSINT |
| topic |
OSINT Ciberinteligencia (seguridad informática) Seguridad informática Cyberintelligence Open source intelligence Adversary profiling Machine learning Sentiment analysis Data science Análisis de sentimientos Aprendizaje automático Ciber inteligencia Ciencia de datos Inteligencia de fuentes abiertas Perfilamiento de adversarios |
| dc.subject.armarc.spa.fl_str_mv |
Ciberinteligencia (seguridad informática) Seguridad informática |
| dc.subject.proposal.eng.fl_str_mv |
Cyberintelligence Open source intelligence Adversary profiling Machine learning Sentiment analysis Data science |
| dc.subject.proposal.spa.fl_str_mv |
Análisis de sentimientos Aprendizaje automático Ciber inteligencia Ciencia de datos Inteligencia de fuentes abiertas Perfilamiento de adversarios |
| description |
Open source intelligence (OSINT) is used to obtain and analyze information related to adversaries, so it can support risk assessments aimed to prevent damages against critical assets. This paper presents a research about different OSINT technologies and how these can be used to perform cyber intelligence tasks. One of the key components in the operation of OSINT tools are the “transforms”, which are used to establish relations between entities of information from queries to different open sources. A set of transforms addressed to the Colombian context are presented, which were implemented and contributed to the community allowing to the law enforcement agencies to develop information gathering process from Colombian open sources. Additionally, this paper shows the implementation of three machine learning models used to perform sentiment analysis over the information obtained from an adversary. Sentiment analysis can be extremely useful to understand the motivation that an adversary can have and, in this way, define proper cyber defense strategies. Finally, some challenges related to the application of OSINT techniques are identified and described. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2021-05-20T23:11:38Z 2021-10-01T17:22:49Z |
| dc.date.available.none.fl_str_mv |
2021-05-20 2021-10-01T17:22:49Z |
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Artículo de revista |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/publishedVersion |
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https://repositorio.escuelaing.edu.co/handle/001/1459 |
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https://doi.org/10.14483/2322939X.13504 |
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Vinculos |
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M. Glassman and M. J. Kang, “Intelligence in the internet age: The emergence and evolu-tion of Open Source Intelligence (OSINT)”, Computers in Human Behavior, vol. 28, no. 2, pp. 673–682, 2012, https://doi.org/10.1016/j.chb.2011.11.014 L. brotherston and A. berlin, “Defensive se-curity handbook: best practices for securing infrastructure”. O’Reilly Media, 2017 W. Alcorn, C. Frichot, and M. Orrù, “The brow-ser hacker’s handbook”,New Jersey: John Wiley and Sons, 2014. M. Gregg, “Certified Ethical Hacker (CEH) Ver-sion 9 Cert Guide” London: Pearson Education, 2017. P. Engebretson, “The basics of hacking and pe-netration testing” Syngressr Publishing, 2013. D. bradbury, “In plain view: open source inte-lligence”, Computers in Human Behavior, no. 4, pp. 5–9, 2011. b. de S. G. Rodrigues, “Open-source intelligen-ce em sistemas SIEM” Lisboa: Universidade de Lisboa, 2015. C. Pérez, “Minería de datos: técnicas y herra-mientas” Paraninfo Cengage Learning, 2007. G. Subramanian, “R Data analysis projects: build end to end analytics systems to get deeper insights from your data”, birmingham: Packt Publishing, 2017. L. Zhang and b. Liu, “Sentiment Analysis and Opinion Mining”. in Encyclopedia of Ma-chine Learning and Data Mining, boston: Springer, 2017, pp. 1152–1161, https://doi.org/10.1007/978-1-4899-7687-1_907 E. Cambria, b. Schuller, Y. Xia, and C. Havasi, “New Avenues in Opinion Mining and Senti-ment Analysis”, IEEE Intelligent Systems, vol. 28, no. 2, pp. 15–21, 2013, https://doi.org/10.1109/MIS.2013.30 A. Ortony, G. L. Clore, and A. Collins, “The cognitive structure of emotions” Cambridge: Cambridge University Press, 1988, https://doi.org/10.1017/CbO9780511571299 R. A. Stevenson, J. A. Mikels, and T. W. Ja-mes, “Characterization of the Affective Nor-ms for English Words by discrete emotional categories”, Behavior Research Methods, vol. 39, no. 4, pp. 1020–1024, 2007, https://doi.org/10.3758/bF03192999 P. D. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, july 2002, pp. 417-424. S. M. Kim and E. Hovy, “Identifying and Analyzing Judgment Opinions”, Association for Computatio-nal Linguistics Stroudsburg, pp. 200–207, 2006, https://doi.org/10.3115/1220835.1220861 Liangxiao Jiang, H. Zhang, and Zhihua Cai, “A Novel bayes Model: Hidden Naive bayes”, IEEE Transactions on Knowledge and Data Enginee-ring, vol. 21, no. 10, pp. 1361–1371, 2009, https://doi.org/10.1109/TKDE.2008.234 Y. Yang and G. I. Webb, “A Comparative Study of Discretization Methods for Naive-bayes Clas-sifiers”, J. Res., vol. 2, p. 267-324, 2007. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and b. Scholkopf, “Support vector machines”, IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18–28, 1998, https://doi.org/10.1109/5254.708428 F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Sur-veys, vol. 34, no. 1, pp. 1–47, 1999, https://doi.org/10.1145/505282.505283 b. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Sum-marization based on Minimum Cuts”, Proce-edings of ACL, pp. 271-278, 2004, https://doi.org/10.3115/1218955.1218990 T. Wilson, J. Wiebe, and P. Hoffmann, “Recogni-zing contextual polarity in phrase-level sentiment analysis”, Proceedings of the conference on Hu-man Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354, 2005, https://doi.org/10.3115/1220575.1220619 H. Wang, D. Can, A. Kazemzadeh, F. bar and S. Narayanan, “A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycl,”. In 50th Annual Meeting of the Association for Computational Linguistics, Jeju Island, july, 2012. C-SPAN, “Robert Mueller on Cyberse-curity” [En línea] Disponible en: ht-tps://www.c-span.org/video/?319726-3/robert-mueller-cybersecurity&start=1876 Departamento Nacional de Planeación, “CONPES 3701 - Lineamientos de Política para Ciberseguridad y Ciberdefensa. Colombia”. Consejo Nacional de Política Económica y So-cial, 2011. R. Rodríguez, “Guerra Asimétrica”. [En línea]. Disponible en: https://dialnet.unirioja.es/des-carga/articulo/4602435.pdf J. Nye, “bound to Lead: The Changing Nature of American Power” Hachette U. basic books, 2016. G. S. Medero, “Ciberespacio y el crimen or-ganizado. Los nuevos desafíos del siglo XXI”, Revista Enfoques, vol.10, no. 16, pp. 71–87, 2012. R. Langner, “Stuxnet: Dissecting a cyberwarfare weapon”, IEEE Security and Privacy, vol. 9, no. 3, pp. 49–51, 2011, https://doi.org/10.1109/MSP.2011.67 G. Friedman, “The next 100 years: a forecast for the 21st century”, Knopf Doubleday Publishing Group, 2009, pp. 193–212. R. Steele, “Handbook of Intelligence Studies” London: Routledge, 2007. |
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Hernandez Mediná, Martin Joseba5eee6d42c837fe2d70e9e88b0b521f600Pinzón Hernández, Cristian Camilo158382a94c73bf7c25639480988b2515600Díaz López, Daniel Orlandoa6116efa385deb85832ad5a8a801ab68600Garcia Ruiz, Juan Carlos1e5e9669d6ab22f310cbfa989a30ea60600Pinto Rico, Ricardo Andrése7cae25174364a79260842503a5d0b75600CTG-Informática2021-05-20T23:11:38Z2021-10-01T17:22:49Z2021-05-202021-10-01T17:22:49Z20181794-211X2322-939Xhttps://repositorio.escuelaing.edu.co/handle/001/1459https://doi.org/10.14483/2322939X.13504https://revistas.udistrital.edu.co/index.php/vinculos/article/view/13504Open source intelligence (OSINT) is used to obtain and analyze information related to adversaries, so it can support risk assessments aimed to prevent damages against critical assets. This paper presents a research about different OSINT technologies and how these can be used to perform cyber intelligence tasks. One of the key components in the operation of OSINT tools are the “transforms”, which are used to establish relations between entities of information from queries to different open sources. A set of transforms addressed to the Colombian context are presented, which were implemented and contributed to the community allowing to the law enforcement agencies to develop information gathering process from Colombian open sources. Additionally, this paper shows the implementation of three machine learning models used to perform sentiment analysis over the information obtained from an adversary. Sentiment analysis can be extremely useful to understand the motivation that an adversary can have and, in this way, define proper cyber defense strategies. Finally, some challenges related to the application of OSINT techniques are identified and described.La Inteligencia de fuentes abiertas (OSINT) es una rama de la ciber inteligencia usada para obtener y analizar información relacionada a posibles adversarios, para que esta pueda apoyar evaluaciones de riesgo y ayudar a prevenir afectaciones contra activos críticos. Este artículo presenta una investigación acerca de diferentes tecnologías OSINT y como estas pueden ser usadas para desarrollar tareas de ciber inteligencia de una nación. Un conjunto de transformadas apropiadas para un contexto colombiano son presentadas y contribuidas a la comunidad, permitiendo a organismos de seguridad adelantar procesos de recolección de información de fuentes abiertas colombianas. Sin embargo, el verdadero aprovechamiento de la información recolectada se da mediante la implementación de tres modelos de aprendizaje automático usados para desarrollar análisis de sentimientos sobre dicha información, con el fin de saber la posición del adversario respecto a determinados temas y así entender la motivación que puede tener, lo cual permite definir estrategias de ciberdefensa apropiadas. Finalmente, algunos desafíos relacionados a la aplicación de técnicas OSINT también son identificados y descritos al respecto de su aplicación por agencias de seguridad del estado.Estudiante Ingeniería de Sistemas. Escuela Colombiana de Ingeniería Julio Garavito. Correo electrónico: ricardo.pinto@mail.escuelaing.edu.co Estudiante Ingeniería de Sistemas. Escuela Colombiana de Ingeniería Julio Garavito. Correo electrónico: martin.hernandez@mail.escuelaing.edu.co Estudiante Ingeniería de Sistemas. Escuela Colombiana de Ingeniería Julio Garavito. Correo electrónico: cristian.pinzon@mail.escuelaing.edu.co Doctor en Informática; profesor asistente, Escuela Colombiana de Ingeniería Julio Garavito. Correo electrónico: daniel.diaz@escuelaing.edu.co Especialista en Seguridad Informática; jefe División de Ciberdefensa, Dirección de Cibernética Naval. Armada Nacional. Correo electrónico: juan.garciaru@armada.mil.coA+T AcTuAlidAd TecnologicA20 páginasapplication/pdfengUniversidad Distrital Francisco José de Caldas-Facultad TecnológicaColombiahttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://revistas.udistrital.edu.co/index.php/vinculos/article/view/13504Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment AnalysisInteligencia de fuentes abierta (OSINT) para operaciones de ciberseguridad. “Aplicación de OSINT en un contexto colombiano y análisis de sentimientosArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85214219515N/AVinculosM. Glassman and M. J. Kang, “Intelligence in the internet age: The emergence and evolu-tion of Open Source Intelligence (OSINT)”, Computers in Human Behavior, vol. 28, no. 2, pp. 673–682, 2012, https://doi.org/10.1016/j.chb.2011.11.014L. brotherston and A. berlin, “Defensive se-curity handbook: best practices for securing infrastructure”. O’Reilly Media, 2017W. Alcorn, C. Frichot, and M. Orrù, “The brow-ser hacker’s handbook”,New Jersey: John Wiley and Sons, 2014.M. Gregg, “Certified Ethical Hacker (CEH) Ver-sion 9 Cert Guide” London: Pearson Education, 2017.P. Engebretson, “The basics of hacking and pe-netration testing” Syngressr Publishing, 2013.D. bradbury, “In plain view: open source inte-lligence”, Computers in Human Behavior, no. 4, pp. 5–9, 2011.b. de S. G. Rodrigues, “Open-source intelligen-ce em sistemas SIEM” Lisboa: Universidade de Lisboa, 2015.C. Pérez, “Minería de datos: técnicas y herra-mientas” Paraninfo Cengage Learning, 2007.G. Subramanian, “R Data analysis projects: build end to end analytics systems to get deeper insights from your data”, birmingham: Packt Publishing, 2017.L. Zhang and b. Liu, “Sentiment Analysis and Opinion Mining”. in Encyclopedia of Ma-chine Learning and Data Mining, boston: Springer, 2017, pp. 1152–1161, https://doi.org/10.1007/978-1-4899-7687-1_907E. Cambria, b. Schuller, Y. Xia, and C. Havasi, “New Avenues in Opinion Mining and Senti-ment Analysis”, IEEE Intelligent Systems, vol. 28, no. 2, pp. 15–21, 2013, https://doi.org/10.1109/MIS.2013.30A. Ortony, G. L. Clore, and A. Collins, “The cognitive structure of emotions” Cambridge: Cambridge University Press, 1988, https://doi.org/10.1017/CbO9780511571299R. A. Stevenson, J. A. Mikels, and T. W. Ja-mes, “Characterization of the Affective Nor-ms for English Words by discrete emotional categories”, Behavior Research Methods, vol. 39, no. 4, pp. 1020–1024, 2007, https://doi.org/10.3758/bF03192999P. D. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, july 2002, pp. 417-424.S. M. Kim and E. Hovy, “Identifying and Analyzing Judgment Opinions”, Association for Computatio-nal Linguistics Stroudsburg, pp. 200–207, 2006, https://doi.org/10.3115/1220835.1220861Liangxiao Jiang, H. Zhang, and Zhihua Cai, “A Novel bayes Model: Hidden Naive bayes”, IEEE Transactions on Knowledge and Data Enginee-ring, vol. 21, no. 10, pp. 1361–1371, 2009, https://doi.org/10.1109/TKDE.2008.234Y. Yang and G. I. Webb, “A Comparative Study of Discretization Methods for Naive-bayes Clas-sifiers”, J. Res., vol. 2, p. 267-324, 2007.M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and b. Scholkopf, “Support vector machines”, IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18–28, 1998, https://doi.org/10.1109/5254.708428F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Sur-veys, vol. 34, no. 1, pp. 1–47, 1999, https://doi.org/10.1145/505282.505283b. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Sum-marization based on Minimum Cuts”, Proce-edings of ACL, pp. 271-278, 2004, https://doi.org/10.3115/1218955.1218990T. Wilson, J. Wiebe, and P. Hoffmann, “Recogni-zing contextual polarity in phrase-level sentiment analysis”, Proceedings of the conference on Hu-man Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354, 2005, https://doi.org/10.3115/1220575.1220619H. Wang, D. Can, A. Kazemzadeh, F. bar and S. Narayanan, “A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycl,”. In 50th Annual Meeting of the Association for Computational Linguistics, Jeju Island, july, 2012.C-SPAN, “Robert Mueller on Cyberse-curity” [En línea] Disponible en: ht-tps://www.c-span.org/video/?319726-3/robert-mueller-cybersecurity&start=1876Departamento Nacional de Planeación, “CONPES 3701 - Lineamientos de Política para Ciberseguridad y Ciberdefensa. Colombia”. Consejo Nacional de Política Económica y So-cial, 2011.R. Rodríguez, “Guerra Asimétrica”. [En línea]. Disponible en: https://dialnet.unirioja.es/des-carga/articulo/4602435.pdfJ. Nye, “bound to Lead: The Changing Nature of American Power” Hachette U. basic books, 2016.G. S. Medero, “Ciberespacio y el crimen or-ganizado. Los nuevos desafíos del siglo XXI”, Revista Enfoques, vol.10, no. 16, pp. 71–87, 2012.R. Langner, “Stuxnet: Dissecting a cyberwarfare weapon”, IEEE Security and Privacy, vol. 9, no. 3, pp. 49–51, 2011, https://doi.org/10.1109/MSP.2011.67G. Friedman, “The next 100 years: a forecast for the 21st century”, Knopf Doubleday Publishing Group, 2009, pp. 193–212.R. 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