Crossing Point of Artificial Intelligence in Cybersecurity
DOI:
https://doi.org/10.18034/ajtp.v2i3.493Keywords:
Artificial intelligence, cybersecurity, data miningAbstract
There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.
Downloads
References
Bibel, W. (2014). Artificial intelligence in a historical perspective. AI Communications, 27(1), 87-102. https://search.proquest.com/docview/1531922017?accountid=35493 DOI: https://doi.org/10.3233/AIC-130576
Han, K., Kang, B., & Im, E. G. (2014). Malware analysis using visualized image matrices. The Scientific World Journal, 2014. https://dx.doi.org/10.1155/2014/132713 DOI: https://doi.org/10.1155/2014/132713
He, D., Chan, S., Zhang, Y., Wu, C., & Wang, B. (2014). How effective are the prevailing attack-defense models for cybersecurity anyway? IEEE Intelligent Systems, 29(5), 14-21. https://dx.doi.org/10.1109/MIS.2013.105 DOI: https://doi.org/10.1109/MIS.2013.105
Kremer, J. (2014). Policing cybercrime or militarizing cybersecurity? Security mindsets and the regulation of threats from cyberspace. Information & Communications Technology Law, 23(3), 220. https://dx.doi.org/10.1080/13600834.2014.970432 DOI: https://doi.org/10.1080/13600834.2014.970432
Lee, R. M., U.S.A.F. (2013). The interim years of cyberspace. Air & Space Power Journal, 27(1), 58-79. https://search.proquest.com/docview/1318929537?accountid=35493
Nourani, V., Hosseini Baghanam, A., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet-artificial intelligence models in hydrology: A review. Journal of Hydrology (Amsterdam), 514, 358-377. https://dx.doi.org/10.1016/j.jhydrol.2014.03.057 DOI: https://doi.org/10.1016/j.jhydrol.2014.03.057
O'Leary, D.,E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96-99. https://dx.doi.org/10.1109/MIS.2013.39 DOI: https://doi.org/10.1109/MIS.2013.39
Straub, J., & Huber, J. (2013). A characterization of the utility of using artificial intelligence to test two artificial intelligence systems. Computers, 2(2), 67-87. https://dx.doi.org/10.3390/computers2020067 DOI: https://doi.org/10.3390/computers2020067
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197-208. https://dx.doi.org/10.24908/ss.v12i2.4776 DOI: https://doi.org/10.24908/ss.v12i2.4776
Zeng, D., & Mao, W. (2014). Supporting global collective intelligence via artificial intelligence. IEEE Intelligent Systems, 29(2), 2-4. https://dx.doi.org/10.1109/MIS.2014.30 DOI: https://doi.org/10.1109/MIS.2014.30
--0--
Downloads
Published
Issue
Section
License
Articles can be read and shared for noncommercial purposes under the following conditions:
- BY: Attribution must be given to the original source (Attribution)
- NC: Works may not be used for commercial purposes (Noncommercial)

