A review of deep learning applied to cybersecurity
DOI:
https://doi.org/10.26423/rctu.v9i1.671Keywords:
Cybersecurity, neural networks, deep learning, internet of things, artificial intelligenceAbstract
This study conceives to present an overview of cybersecurity from the perspective of neural networks and deep learning techniques according to the diverse current needs in computer security environments. It also discusses the applicability of these techniques in various cybersecurity works, such as intrusion detection, malware or botnet identification, phishing, cyber-attack prediction, denial of service, cyber anomalies, etc. In conclusion, four algorithms applicable to Cybersecurity are highlighted and recommended as a knowledge base and facility for future research within the scope of our study in the field. The ultimate target of this research is to serve as a reference point and guide for academia and practitioners in cybersecurity industries from the deep learning point of view.
Downloads
References
NAVARRO, Andrés; URCUQUI, Christian; OSORIO, José y GARCÍA, Melisa. Ciberseguridad: un enfoque desde la ciencia de datos[En línea]. Universidad Icesi, 2019 [Consulta: 28 abr. 2022]. Disp. desde DOI: 10.18046/EUI/ee.4.2018 DOI: https://doi.org/10.18046/EUI/ee.4.2018
SECURITY, IBM. IBM: Cost of a Data Breach Report 2019. Computer Fraud & Security [En línea]. 2019, vol. 2019, n.o 8, págs. 4-4. ISSN 1361-3723. Disp. desde DOI: 10.1016/s1361-3723(19)30081-8 DOI: https://doi.org/10.1016/S1361-3723(19)30081-8
MORGAN, Steve. Cybersecurity Almanac: 100 Facts, Figures, Predictions and Statistics. Cybercrime Magazine [En línea]. 2022 [Consulta: 28 abr. 2022]. Disponible en: https://cybersecurityventures.com/cybersecurity-almanac-2022.
AFTERGOOD, Steven. Cybersecurity: The cold war online. Nature 2017 547:7661 [En línea]. 2017, vol. 547, n.o 7661, págs. 30-31. ISSN 1476-4687. Disp. desde DOI: doi:10.1038/547030a DOI: https://doi.org/10.1038/547030a
FOROUGHI, Farhad y LUKSCH, Peter. Data Science Methodology for Cybersecurity Projects [En línea]. 2018, págs. 1-14. Disp. desde DOI: 10.5121/csit.2018.80401 DOI: https://doi.org/10.5121/csit.2018.80401
Tendencias de ciberseguridad para 2020. Panda Security [En línea]. 2019 [Consulta: 29 abr. 2022]. Disponible en: https://www.pandasecurity.com/spain/mediacenter/mobile-news/tendencias-ciberseguridad-2020/.
XIN, Yang; KONG, Lingshuang; LIU, Zhi; CHEN, Yuling; LI, Yanmiao; ZHU, Hongliang; GAO, Mingcheng; HOU, Haixia y WANG, Chunhua. Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access [En línea]. 2018, vol. 6, págs. 35365-35381. ISSN 21693536. Disponible en: 10.1109/ACCESS.2018.2836950 DOI: https://doi.org/10.1109/ACCESS.2018.2836950
JOYANES, Luis. Ciberseguridad: la colaboración público-privada en la era de la cuarta revolución industrial (Industria 4.0 versus ciberseguridad 4.0). Cuadernos de estrategia. 2017, n.o 185, págs. 19-64. Disponible en: https://dialnet.unirioja.es/servlet/articulo?codigo=6115620
SARKER, Iqbal; KAYES, A.; BADSHA, Shahriar; ALQAHTANI, Hamed; WATTERS, Paul y NG, Alex. Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data [En línea]. 2020, vol. 7, n.o 1. ISSN 21961115. Disp. desde DOI: 10.1186/s40537-020-00318-5 DOI: https://doi.org/10.1186/s40537-020-00318-5
SARKER, Iqbal; FURHAD, M. y NOWROZY, Raza. AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions.SN Computer Science [En línea]. 2021, vol. 2, n.o 3, págs. 1-18. ISSN 2662-995X. Disp. desde DOI: 10.1007/s42979-021-00557-0 DOI: https://doi.org/10.1007/s42979-021-00557-0
AGARAP, Abien. Deep Learning using Rectified Linear Units (ReLU) [En línea]. 2018. Disponible en: http://arxiv.org/abs/1803.08375.
ZAJMI, Leke; AHMED, Falah y JAHARADAK, Amril. Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation and Neural Networks. Applied Computational Intelligence and Soft Computing [En línea]. 2018. ISSN 16879732. Disp. desde DOI: 10.1155/2018/9547212
DE ALMEIDA, Felipe; MORENO, Edward; MACEDO, Hendrik; DE BRITO, Ricardo; DO NASCIMIENTO, Filipe y OLIVEIRA, Flavio. Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks. Applied Computational Intelligence and Soft Computing [En línea]. 2018. ISSN 16879732. Disp. desde DOI: 10.1155/2018/9547212
HODO, Elike; BELLEKENS, Xavier; HAMILTON, Andrew; DUBOUILH, Pierre-Louis; IORKYASE, Ephraim; TACHTATZIS, Christos y ATKINSON, Robert. Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks. Applied Computational Intelligence and Soft Computing [En línea]. 2018. ISSN 16879732. Disp. desde DOI: 10.1155/2018/9547212 DOI: https://doi.org/10.1155/2018/9547212
PANIAGUA, Omar; HERNÁNDEZ, Juan; RUIZ, Juan; REYES, Mauricio; FERREIRA, Heberto y HERNÁNDEZ, Anastasio. Diseño De Un Prototipo Iot Para Pruebas De Penetración Y Monitoreo De La Seguridad En Un Sistema De Domótica [En línea]. 2019, pág. 14. Disponible en: https://www.researchgate.net/profile/Juan_Roberto_Hernandez_Herrera2/publication/339136248_Diseno_de_un_prototipo_IoT_para_pruebas_de_penetracion_y_monitoreo_de_la_seguridad_en_un_sistema_de_domotica/links/5e403bbda6fdccd9659620d4/Diseno-de-un-prototipo-I
SUSILO, Bambang y SARI, Riri. Intrusion detection in IoT networks using deep learning algorithm. Information (Switzerland) [En línea]. 2020, vol. 11, n.o 5. ISSN 20782489. Disp. desde DOI: 10.3390/INFO11050279 DOI: https://doi.org/10.3390/info11050279
YAN, Jinpei; QI, Yong y RAO, Qifan. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks [En línea]. 2018. ISSN 19390122. Disp. desde DOI: 10.1155/2018/7247095
MCLAUGHLIN, Niall; DEL RINCÓN, Jesús; KANG, Boo; YERIMA, Suleiman; MILLER, Paúl; SEZER, Sakir; SAFAEL, Yeganeh; TRICKEL, Erik; ZHAO, Ziming; DOUPE, Adam y AHN, Gail. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks [En línea]. 2018. ISSN 19390122. Disp. desde DOI: 10.1155/2018/7247095 DOI: https://doi.org/10.1155/2018/7247095
ADEBOWALE, Moruf; LWIN, Khin y HOSSAIN, M. Intelligent phishing detection scheme using deep learning algorithms. Journal of Enterprise Information Managemen [En línea]. 2020. ISSN 17410398. Disp. desde DOI: 10.1108/JEIM-01-2020-0036 DOI: https://doi.org/10.1108/JEIM-01-2020-0036
WU, Peliun; GUO, Hui y BUCKLAND, Richard. A Transfer Learning Approach for Network Intrusion Detection. 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 [En línea]. 2019, págs. 281-285. Disp. desde DOI: 10.1109/ICBDA.2019.8713213 DOI: https://doi.org/10.1109/ICBDA.2019.8713213
NAHMIAS, Daniel; COHEN, Aviad; NISSIM, Nir y ELOVICI, Yuval. Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments. Neural Networks [En línea]. 2020, vol. 124, págs. 243-257. ISSN 18792782. Disp. desde DOI: 10.1016/j.neunet.2020.01.003 DOI: https://doi.org/10.1016/j.neunet.2020.01.003
NAHMIAS, Daniel; COHEN, Aviad; NISSIM, Nir y ELOVICI, Yuval. TrustSign: Trusted Malware Signature Generation in Private Clouds Using Deep Feature Transfer Learning. Proceedings of the International Joint Conference on Neural Networks [En línea]. 2019, págs. 1-8. ISSN 18792782. Disp. desde DOI: 10.1109/IJCNN.2019.8851841 DOI: https://doi.org/10.1109/IJCNN.2019.8851841
ZHAO, Juan; SHETTY, Sachin; PAN, Jan; KAMHOUA KAMHOUA, Charles y KWIAT, Kevin. Transfer learning for detecting unknown network attacks. Eurasip Journal on Information Security [En línea]. 2019, n.o 1. ISSN 2510523X. Disp. desde DOI: 10.1186/s13635-019-0084-4 DOI: https://doi.org/10.1186/s13635-019-0084-4
SARKER, Iqbal. Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective. SN Computer Science [En línea]. 2021, vol. 2, n.o 3. ISSN 2662-995X. Disp. desde DOI: 10.1007/s42979-021-00535-6 DOI: https://doi.org/10.1007/s42979-021-00535-6
SARKER, Iqbal; KAYES, A y WATTERS, Paul. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data [En línea]. 2019, vol. 6, n.o 1. ISSN 21961115. Disp. desde DOI: 10.1186/s40537-019-0219-y DOI: https://doi.org/10.1186/s40537-019-0219-y
GEETHA, R. y THILAGAM, T. A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security. Archives of Computational Methods in Engineering [En línea]. 2021, vol. 28, n.o 4, págs. 2861-2879. ISSN 18861784. Disp. desde DOI: 10.1007/s11831-020-09478-2 DOI: https://doi.org/10.1007/s11831-020-09478-2
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Daniel Ivan Quirumbay Yagual; Carlos Castillo Yagual, Ivan Coronel Suárez
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
El titular de los derechos de autor de la obra, otorga derechos de uso a los lectores mediante la licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional. Esto permite el acceso gratuito inmediato a la obra y permite a cualquier usuario leer, descargar, copiar, distribuir, imprimir, buscar o vincular a los textos completos de los artículos, rastrearlos para su indexación, pasarlos como datos al software o usarlos para cualquier otro propósito legal.
Cuando la obra es aprobada y aceptada para su publicación, los autores conservan los derechos de autor sin restricciones, cediendo únicamente los derechos de reproducción, distribución para su explotación en formato de papel, así como en cualquier otro soporte magnético, óptico y digital.
