Cyber Threats in Artificial Intelligence
DOI:
https://doi.org/10.26438/ijcse/v8i9.4347Keywords:
Remote Network Monitoring, Artificial Intelligence, Sequential Feature Selection, Behavioral Assessment, Cyber Threats Intelligence Neural NetworkAbstract
In the state of affairs of Digital Security there has been an alternate from the vicinity of Digital Guiltiness to the part of Digital War in the route of the most present day couple of years. As indicated by the new difficulties, the master network has two principle draws near to embrace the way of thinking and techniques for Military Insight, and to utilize Man-made brainpower strategies for balance of Digital Assaults. This paper portrays a portion of the outcomes got at Specialized the American College of Sofia in the usage of undertaking identified with the use of insightful techniques for expanding the security in PC systems. The investigation of the achieve ability of different Man-made reasoning strategies has demonstrated that a technique that is similarly successful for all phases of the Digital Knowledge can't be distinguished. While for Strategic Digital Dangers Knowledge has been chosen and examined a Multi-Specialist Framework, the Repetitive Neural Systems are presented for the necessities of Operational Cyber Threats Artificial Intelligence.
References
[1] Elike Hodo, Xavier Bellekens, Ephraim Iorkyase, Andrew Hamilton, Christos Tachtatzis and Robert Atkinson. Machine Learning Approach for Detection of nonTor Traffic. Journal of Cyber Security, Vol. 6.2, pp 171–194, November 2017. doi: 10.13052/jcsm2245- 1439.624
[2] [RFC 1757] Remote Network Monitoring Management Information Base, Carnegie Mellon University, 1995
[3] Brian P. Kime Threat Intelligence: Planning and Direction, SANS Institute, 2015
[4] Hammon P.S. and Sa V.R. de, Preprocessing and meta- classification for brain-computer interfaces, IEEE Transactions on Biomedical Engineering, 54(3), 2007.
[5] Koprinkova-Hristova P., Bozhkov L. and Georgieva P., Echo State Networks for feature selection in affectivecomputing
[6] Republic of Bulgaria: National Cyber Security Strategy “Cyber Resilient Bulgaria 2020”, 2016-03 NCSS Bulgaria final draft v 5 3, Bulgarian government, 2016
[7] Cyber Intelligence: Setting the Landscape for an emerging Discipline, Intelligence and National Security Alliance, INSA, 2011
[8] Operational Level of Cyber Intelligence, INSA, 2013
[9] Operational Cyber Intelligence, INSA, 2014
[10] Liu Y., Sourina O. and Nguyen M. K., Real-time EEG-based human emotion recognition and visualization, in Proceedings of the International Conference on Cyberworlds (CW ’10), 2010, Singapore
[11] L. Bozhkov, P. Georgieva Classification models of emotional bio signals evoked while viewing affective pictures
[12] Erik Hjelmvik, Passive Network Security Analysis with Network Miner, (IN)SECURE Magazine, no. 18, pp. 18–21,2008.
[13] Lukosevicius M. and Jaeger H., Reservoir computing approaches to recurrent neural network training, Computer Science Review, vol. 3, 2009
[14] ENISA Threats Landscape Report 2016: 15 Top Cyber-Threats and Trends, ENISA, 2017
[15] Guyon I. and Elisseeff A. An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, vol. 3, 2003
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