Deeplearning Mechanism For Cyber Security Vulnerability Assessments And Risk Analysis

  • Kumar Parasuraman, A. Anbarasa Kumar, A.Udayakumar

Abstract

The homogeneous data fusion Cyber Computing based intelligent directional mesh network architecture, routers, gateways, switches, portals and even actuators can be conceivably utilized as end Cyber nodes. The Cyber nodes will remove various features from continuous real-time state data of network nodes utilizing prepared machine learning models and settle on scholarly decisions to choose an ideal communication way considering the limitations, for example, power spending and spectrum occupation. DMN (Directional Mesh Network) become increasingly flexible to the local environment and strong to spectrum changes. The current research is going on improving the appraisal performance in presence of joint node and link attack. Because the generation of original infrastructures with new services and connections. That requires more frequent vulnerability assessments. In the prior work, they have the network estimation approaches on either only network or node attacks presence. They did not consider the arrangement of the node and link attack situations. In this paper going to concentrate on the joint node and link attack and to manage the minimum cost of node and link in the network to gain the networking process.

Published
2020-06-02
How to Cite
Kumar Parasuraman, A. Anbarasa Kumar, A.Udayakumar. (2020). Deeplearning Mechanism For Cyber Security Vulnerability Assessments And Risk Analysis. International Journal of Advanced Science and Technology, 29(9s), 6501 - 6509. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/20345