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Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach

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submitted on 2023-12-25, 10:18 and posted on 2024-01-10, 12:26 authored by Nagendra Prabhu Selvaraj, Sivakumar Paulraj, Parthasarathy Ramadass, Rajesh Kaluri, Mohammad Shorfuzzaman, Abdulmajeed Alsufyani, Mueen Uddin

Many cloud service providers offer access to versatile, dependable processing assets following a compensation as-you-go display. Investigation into the security of the cloud focusses basically on shielding genuine clients of cloud administrations from assaults by outer, vindictive clients. Little consideration is given to restrict malicious clients from utilizing the cloud to dispatch assaults, for example, those as of now done by botnets. These assaults incorporate propelling a DDoS attack, sending spam and executing click extortion. Bots’ detection in the cloud environment is a complex process. The purpose of this study was to create a multi-layered architecture that could detect a variety of existing and emerging botnets. The goal is to be able to detect a larger range of bots and botnets by relying on several techniques called trust model. On this work, the port access verification in trust model is achieved by a Heuristic factorizing algorithm which verifies the port accessibility between client-end-user and client server. Further, back-off features are extracted from the particular node and all these structures are trained and categorized with a Co-Active Neuro Fuzzy Expert System (CANFES) classifier. The performance of the proposed bot detection system in the internet environment is analyzed latency, detection rate, packet delivery ration, energy availability and precision.

Other Information

Published in: Electronics
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/electronics11152350

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Computing and Information Technology - UDST
  • College of the North Atlantic Qatar (-2022)

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