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Abstract

According to the numerous present-day requirements in computer security environments, this paper gives an overview of cybersecurity from the standpoint of neural networks and deep learning algorithms. It covers how these techniques can be used in a variety of cybersecurity tasks, including intrusion detection, malware or botnet identification, phishing, cyber attack prediction, denial of service, and cyber anomalies, among others. The analytical-synthetic method was used for this investigation to find the best cybersecurity solutions.The findings emphasize and suggest cybersecurity-related algorithms as a knowledge foundation and resource for upcoming field research that falls within the purview of this study. From the perspective of deep learning, this research serves as a resource and a manual for academics and professionals in the cyber security industry

Keywords

deep learning internet of things artificial intelligence

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Serri Ismael Hamad. (2023). A review of deep learning applied to cyber security. Texas Journal of Engineering and Technology, 22, 49–57. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4272

References

  1. NAVARRO, Andrés; URCUQUI, Christian; OSORIO, José and GARCÍA, Melisa. Cybersecurity: a data science approach [Online]. Icesi University, 2019 [Accessed: 28 abr. 2022]. Disp. from DOI: doi:10.18046/EUI/ee.4.2018.
  2. MORGAN, Steve. Cybersecurity Almanac: 100 Facts, Figures, Predictions and Statistics. Cybercrime Magazine [Online]. 2022 [Accessed: 28 apr. 2022]. Available in:https: // cybersecurityventures. com/cybersecurity-almanac-2022.
  3. AFTERGOOD, Steven. Cybersecurity: The cold war online. Nature 2017 547:7661 [Online]. 2017, vol. 547, no. 7661, pp. 30-31. ISSN 1476-4687. Disp. from DOI:doi:10. 1038 / 547030a.
  4. FOROUGHI, Farhad and LUKSCH, Peter. Data Science Methodology for Cybersecurity Projects [Online]. 2018, pp. 1-14. Disp. from DOI: 10.5121/csit.2018.80401.
  5. Cybersecurity trends for 2020Panda Security [Online]. 2019 [Query:Apr 29 2022]. Available at: https://www. pandasecurity . com / spain / mediacenter / mobile - news / trends - cybersecurity-2020/.
  6. XIN, Yang; KONG, Lingshuang; LIU, Zhi; CHEN, Yuling; LI, Yanmiao; ZHU, Hongliang; GAO, Mingcheng; HOU, Haixia and WANG, Chunhua. Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access [Online]. 2018, vol. 6, pp. 35365-35381. ISSN 21693536. Available in: 10 . 1109/ACCESS. 2018.2836950.
  7. JOYANES, Luis. Cybersecurity: public-private collaboration in the era of the fourth industrial revolution (Industry 4.0 versus cybersecurity 4.0). Strategy notebooks. 2017, no. 185, pp. 19-64. Available at: https://dialnet.unirioja.es/servlet/articulo?codigo=6115620.
  8. SARKER, Iqbal; KAYES, A.; BADSHA, Shahriar; ALQAHTANI, Hamed; WATTERS, Paul and NG, Alex. Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data [Online]. 2020, vol. 7, no. 1. ISSN 21961115. Disp. from DOI: 10.1186/s40537-020-00318-5.
  9. SARKER, Iqbal; FURHAD, M. and NOWROZY, Race. AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions. SN Computer Science [Online]. 2021, vol. 2, no. 3, pp. 1-18. ISSN 2662-995X. Disp. from DOI: 10. 1007 / s42979-021-00557-0.
  10. AGARAP, Okay. Deep Learning using Rectified Linear Units (ReLU) [Online]. 2018. Available at: http://arxiv.org/abs/1803.08375.
  11. ZAJMI, Leke; AHMED, Falah and JAHARADAK, Amril. Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks. Applied Computational Intelligence and Soft Computing [Online]. 2018. ISSN 16879732. Available. from DOI: 10. 1155 / 2018/9547212.
  12. DE ALMEIDA, Felipe; MORENO, Edward; MACEDO, Hendrik; DE BRITO, Ricardo; DO NASCIMENTO, Filipe and OLIVEIRA, Flavio. Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks. Applied Computational Intelligence and Soft Computing [Online]. 2018. ISSN 16879732. Available. from DOI: 10.1155/2018/9547212.
  13. HODO, Elike; BELLEKENS, Xavier; HAMILTON, Andrew;
  14. DUBOUILH, Pierre-Louis; IORKYASE, Ephraim; TACHTATZIS, Christos and ATKINSON, Robert. Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks. Applied Computational Intelligence and Soft Computing [Online]. 2018. ISSN 16879732. Available. from DOI: 10.1155/2018/9547212.
  15. PANIAGUA, Omar; HERNANDEZ, Juan; RUIZ, Juan; REYES, Mauricio; FERREIRA, Hebertoand HERNANDEZ, Anastasio. Design of an Iot Prototype for Penetration Testing and Security Monitoring in a Home Automation System [Online]. 2019, p. 14. Available at: https : / / www . researchgate . net / profile / Juan _ Roberto _ Hernandez _ Herrera2 / publication / 339136248 _ Design _ of _ an IoT _ prototype _ for _ penetration _ tests _ and _ monitoring _ of _ security _ in _ a _ home automation _ system / links / 5e403bbda6fdccd9659620d4 / Design - of - an-I-prototype.
  16. SUSILO, Bambang and SARI, Riri. Intrusion detection in IoT networks using deep learning algorithm. Information (Switzerland) [Online]. 2020, vol. 11, no. 5. ISSN 20782489. Disp. from DOI: 10.3390/INFO11050279.
  17. YAN, Jinpei; QI, Yong and RAO, Qifan. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks [Online]. 2018. ISSN 19390122. Available. from DOI: 10.1155/2018/7247095.
  18. MCLAUGHLIN, Niall; DEL RINCON, Jesus; KANG, Boo; YERIMA, Suleiman; MILLER, Paul; SEZER, Sakir;
  19. SAFAEL, Yeganeh; TRICKEL, Erik; ZHAO, Ziming; DOUPE, Adam and AHN, Gail. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks [Online]. 2018. ISSN 19390122. Available. from DOI:10.1155/2018/7247095.
  20. ADEBOWALE, Moruf; LWIN, Khin and HOSSAIN, M. Intelligent phishing detection scheme using deep learning algorithms. Journal of Enterprise Information Management [Online]. 2020. ISSN 17410398. Available. from DOI: 10.1108/JEIM-01-2020-0036.
  21. WU, Peliun; GUO, Hui and BUCKLAND, Richard. A Transfer Learning Approach for Network Intrusion Detection. 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 [Online]. 2019, pp. 281-285. Disp. from DOI: 10.1109/ICBDA.2019.8713213.
  22. NAHMIAS, Daniel; COHEN, Aviad; NISSIM, Nir and ELOVICI, Yuval. Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments. Neural Networks [Online]. 2020, vol. 124, pp. 243-257. ISSN 18792782. Disp. from DOI: 10.1016/j.neunet.2020. 01.003.
  23. NAHMIAS, Daniel; COHEN, Aviad; NISSIM, Nir and ELOVICI, Yuval. TrustSign: Trusted Malware Signature Generation in Private Clouds Using Deep Feature Transfer Learning. Proceedings of the International Joint Conference on Neural Networks [Online]. 2019, pp. 1-8. ISSN 18792782. Disp. from DOI: 10.1109/IJCNN.2019.8851841.
  24. ZHAO, Juan; SHETTY, Sachin; BREAD, Jan; KAMHOUA KAMHOUA, Charles and KWIAT, Kevin. Transfer learning for detecting unknown network attacks. Eurasip Journal on Information Security [Online]. 2019, no. 1. ISSN 2510523X. Disp. from DOI: 10. 1186/s13635-019-0084-4.
  25. SARKER, Iqbal. Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective. SN Computer Science [Online]. 2021, vol. 2,No. 3. ISSN 2662-995X. Disp. from DOI:10.1007/s42979-021-00535-6.
  26. SARKER, Iqbal; KAYES, A. and WATTERS, Paul. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data [Online]. 2019, vol. 6, no. 1. ISSN21961115. Avail. from DOI: 10.1186/s40537-019-0219-y.
  27. GEETHA, R. and THILAGAM, T. A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security. Archives of Computational Methods in Engineering [Online]. 2021, vol. 28, no 4,p. 2861-2879. ISSN 18861784. Disp. from DOI: 10.1007/s11831-020-09478-2