Review Study on Different Attack Strategies of Worm in a Network

Mondal, Avijit; Das, Arnab Kumar; Nath, Sayan; Goswami, Radha Tamal

doi:10.14704/web/v17i2/web17038

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…Since machine learning algorithms can find patterns in the data, learning new patterns and acts appropriately, this could be used to develop adaptable access control. Baris Yuce and Yacine Rezgui used ANN and genetic algorithm to provide adaptability [26] [27]. Recursive neural Network and deep learning concepts are also used for rule extraction [28].…”

Section: Machine Learningmentioning

confidence: 99%

NdRAdAC: Need based Access Control Framework for an Emergency Response System

Srivastava¹

2021

TURCOMAT

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Access control is easy to implement in a static system with resource-role mapping and known policies. It becomes challenging if the system is dynamic and volatile, which means there are unpredictability in the workflow. Existing role based and attribute- based access control systems are very efficient in static and predictable situations. But they are not effective in a dynamic situation. Researchers over the last two decades have tried to propose various probabilistic based, machine learning based and decision theory-based access control to prove adaptability in their access control methods. But there are existing gaps in operational needs and proposed adaptability methods. Under regular scenario access control system may work based on the policies or decided roles. Only if there is a genuine need, then access control should switch to adaptable solutions. Also, a true adaptable system should not allow human intervention, the system should be able to understand the genuineness of the requester and take decisions whether access should be granted or not. In this paper with the help of a disaster management case study, a need-based access control framework – NdRAdAC is proposed. It evaluates the genuineness of the requester and acts appropriately. An ontology-based access control for an emergency response system is developed, which can help the disaster management system to coordinate with different hospitals and help in transferring patient data from one hospital to another if needed. It ensures that data requester is authenticated with the help of access control module. The framework is tested for three main parameters: Adaptability, Consistency and Computational Efficiency. It was found that framework was accurately adaptable, consistent with all the different types of cases and computationally efficient.

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Section: Machine Learningmentioning

confidence: 99%