Herramienta para identificar vulnerabilidades en aplicaciones ofimáticas de Windows mediante técnicas de procesamiento de lenguaje natural

Tarazona Bustamante, Cristian Camilo

Herramienta para identificar vulnerabilidades en aplicaciones ofimáticas de Windows mediante técnicas de procesamiento de lenguaje natural

dc.contributor.advisor	Gamba González, Yamid Gabriel
dc.contributor.author	Tarazona Bustamante, Cristian Camilo
dc.coverage.campus	UNAB Campus Bucaramanga	spa
dc.date.accessioned	2022-03-25T20:54:06Z
dc.date.available	2022-03-25T20:54:06Z
dc.date.issued	2020
dc.degree.name	Ingeniero de Sistemas	spa
dc.description.abstract	Las vulnerabilidades en las aplicaciones son actualmente encontradas por medio de un fallo que es explotado por ciberdelincuentes afectándolas y por ende los datos que estas contienen , con el fin de identificar cuáles son las vulnerabilidades que tiene una aplicación en este caso ofimáticas , se logra haciendo uso de técnicas de procesamiento de lenguaje natural que permiten obtener la frecuencia con que se repite una vulnerabilidad mediante el conteo de sus palabras que son grupadas por n-gramas y lo que permite visualizar la aparición e identificación de vulnerabilidades en las diferentes aplicaciones que son ejecutadas en Microsoft Office como Microsoft Word , Microsoft Excel , Power Point , Skype, Microsoft Teams y Outlook.	spa
dc.description.abstractenglish	Vulnerabilities in applications are currently found through a bug that is exploited by cybercriminals affecting them and therefore the data they contain, in order to identify which are the vulnerabilities that an application has, in this case office automation, is achieved by using of natural language processing techniques that allow obtaining the frequency with which a vulnerability is repeated by counting its words that are grouped by n-grams and which allows visualizing the appearance and identification of vulnerabilities in the different applications that are executed in Microsoft Office such as Microsoft Word, Microsoft Excel, Power Point, Skype, Microsoft Teams, and Outlook. The vulnerabilities in the applications are currently found through a failure that is exploited by cybercriminals affecting them and therefore the data they contain, in order to identify themselves are the vulnerabilities that an application has in this case office automation, it is achieved by using natural language processing techniques that allow obtaining the frequency with which a vulnerability is repeated the content of its words by means of which they are grouped by n-grams and which allows visualizing the appearance and identification of vulnerabilities in the different applications that are executed in Microsoft Office such as Microsoft Word, Microsoft Excel, Power Point, Skype, Microsoft Teams and Outlook.	spa
dc.description.degreelevel	Pregrado	spa
dc.description.learningmodality	Modalidad Presencial	spa
dc.description.tableofcontents	1 PLANTEAMIENTO DEL PROBLEMA ................................................................. 8 2 JUSTIFICACIÓN ................................................................................................... 9 3 OBJETIVOS ........................................................................................................ 10 3.1 OBJETIVO GENERAL ................................................................................. 10 3.2 OBJETIVOS ESPECÍFICOS ....................................................................... 10 4 ESTADO DEL ARTE .......................................................................................... 11 4.1 REVISIÓN SISTEMÁTICA DE LITERATURA ............................................ 11 4.2 RESULTADOS DE INVESTIGACIÓN ......................................................... 11 5 MARCO REFERENCIAL .................................................................................... 15 5.1 MARCO CONCEPTUAL .............................................................................. 15 5.2 MARCO TEORICO ...................................................................................... 16 5.3 MARCO LEGAL ........................................................................................... 25 5.3.1 Ley 1581 de 2012 Protección de Datos ............................................ 25 6 METODOLOGIA ................................................................................................. 26 7 DESARROLLO Y RESULTADOS...................................................................... 29 7.1 PRIMERA FASE .......................................................................................... 29 7.1.1 Vectores de ataque en Microsoft Office ........................................... 29 7.1.2 Vulnerabilidades en Microsoft Office ............................................... 31 7.1.3 Caracterización de las Bases de datos de Vulnerabilidades ........ 33 7.2 SEGUNDA FASE ......................................................................................... 33 7.3 TERCERA FASE.......................................................................................... 34 CONCLUSIONES ....................................................................................................... 48 REFERENCIAS BIBLIOGRAFICAS .......................................................................... 50	spa
dc.format.mimetype	application/pdf	spa
dc.identifier.instname	instname:Universidad Autónoma de Bucaramanga - UNAB	spa
dc.identifier.reponame	reponame:Repositorio Institucional UNAB	spa
dc.identifier.repourl	repourl:https://repository.unab.edu.co	spa
dc.identifier.uri	http://hdl.handle.net/20.500.12749/16074
dc.language.iso	spa	spa
dc.publisher.faculty	Facultad Ingeniería	spa
dc.publisher.grantor	Universidad Autónoma de Bucaramanga UNAB	spa
dc.publisher.program	Pregrado Ingeniería de Sistemas	spa
dc.relation.references	Awad, Y., Nassar, M., & Safa, H. (2018). Modeling Malware as a Language. 2018 IEEE International Conference on Communications (ICC), 1–6	spa
dc.relation.references	Bernard. (s.f.). Obtenido de Forbes.com: https://www.forbes.com/sites/bernardmarr/2018/10/01/what-is-deeplearning-ai-a-simple-guide-with-8-practical-examples/#32389f338d4b	spa
dc.relation.references	Ciberseguridad. (2017). Obtenido de https://ciberseguridad.com/amenzas/vulnerabilidades/desbordamient o-buffer/	spa
dc.relation.references	Cisco. (2016). Obtenido de https://www.cisco.com/c/en/us/products/security/advanced-malwareprotection/what-is-malware.html	spa
dc.relation.references	Cisco. (2017). Obtenido de www.netacad.com/es/security/introductioncybersecurity/vulnerability	spa
dc.relation.references	Cisco. (2019). Obtenido de https://tools.cisco.com/security/center/resources/virus_differences	spa
dc.relation.references	Cordero, P. (2008). academa.edu. Obtenido de https://www.academia.edu/12029545/APLICACIONES_PRÁCTICAS_ UTILIZANDO_MICROSOFT_EXCEL_Y_WEKA	spa
dc.relation.references	Microsoft. (2019). Obtenido de https://docs.microsoft.com/es-es/analysisservices/data-mining/data-mining-concepts?view=asallproductsallversions	spa
dc.relation.references	Microsoft. (2019). Obtenido de https://docs.microsoft.com/enus/azure/architecture/data-guide/relational-data/etl	spa
dc.relation.references	Minddata. (2018). Obtenido de https://minddata.org/what-is-ai-mit-stanfordharvard-cmu-Brian-Ka-Chan-AI	spa
dc.relation.references	Oracle. (2017). Obtenido de https://www.oracle.com/co/database/what-is-arelational-database/	spa
dc.relation.references	Oracle. (2017). Obtenido de https://www.oracle.com/artificialintelligence/what-is-machine-learning.html	spa
dc.relation.references	Oracle. (2017). Oracle. Obtenido de https://www.oracle.com/co/database/what-is-a-relational-database/	spa
dc.relation.references	Oracle. (2018). Oracle. Obtenido de https://www.oracle.com/co/businessanalytics/	spa
dc.relation.references	Oracle. (2018). oracle.com. Obtenido de https://www.oracle.com/co/businessanalytics/	spa
dc.relation.references	Oracle. (2018). Oracle.com. Obtenido de https://www.oracle.com/co/business-analytics/	spa
dc.relation.references	Oracle. (2019). Oracle.com/co/data-science. Obtenido de https://www.oracle.com/co/data-science/what-is-data-science.html	spa
dc.relation.references	Urbina, G. B. (2017). Google Books. Obtenido de https://books.google.com.co/books?id=IhUhDgAAQBAJ&pg=PA160&l pg=PA160&dq=ataque+entrada+no+validada+de+datos&source=bl& ots=0WSD1CrgKq&sig=ACfU3U2OdoJ2GMi419SlPhRDFrOHTBkqlw &hl=es&sa=X&ved=2ahUKEwjO_pr936fpAhWEmeAKHThLDF0Q6AE wBHoECAoQAQ#v=onepage&q=ataque%2	spa
dc.rights.accessrights	info:eu-repo/semantics/openAccess	spa
dc.rights.creativecommons	Atribución-NoComercial-SinDerivadas 2.5 Colombia	*
dc.rights.local	Abierto (Texto Completo)	spa
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/2.5/co/	*
dc.subject.keywords	Systems engineer	spa
dc.subject.keywords	Technological innovations	spa
dc.subject.keywords	Application vulnerabilities	spa
dc.subject.keywords	Cyber criminals	spa
dc.subject.keywords	Applications	spa
dc.subject.keywords	Operational systems	spa
dc.subject.keywords	System programs	spa
dc.subject.keywords	Natural language	spa
dc.subject.keywords	Computational linguistics	spa
dc.subject.lemb	Ingeniería de sistemas	spa
dc.subject.lemb	Innovaciones tecnológicas	spa
dc.subject.lemb	Sistemas operacionales	spa
dc.subject.lemb	Programas del sistema	spa
dc.subject.lemb	Lenguaje natural	spa
dc.subject.lemb	Lingüística computacional	spa
dc.subject.proposal	Vulnerabilidades en aplicaciones	spa
dc.subject.proposal	Ciberdelincuentes	spa
dc.subject.proposal	Microsoft office	spa
dc.subject.proposal	Aplicaciones	spa
dc.title	Herramienta para identificar vulnerabilidades en aplicaciones ofimáticas de Windows mediante técnicas de procesamiento de lenguaje natural	spa
dc.title.translated	Tool to identify vulnerabilities in Windows office applications using natural language processing techniques	spa
dc.type.coar	http://purl.org/coar/resource_type/c_7a1f
dc.type.driver	info:eu-repo/semantics/bachelorThesis
dc.type.hasversion	info:eu-repo/semantics/acceptedVersion
dc.type.local	Trabajo de Grado	spa
dc.type.redcol	http://purl.org/redcol/resource_type/TP