Generalized Defensive Modeling of Malware Propagation in WSNs Using Atangana-Baleanu-Caputo (ABC) Fractional Derivative

The malware spreading in Wireless Sensor Network (WSN) has lately attracted the attention of many researchers as a hot problem in nonlinear systems. WSN is a collection of sensor nodes that communicate with each other wirelessly. These nodes are linked in a decentralised and distributed structure, a...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Srivastava, Vineet, Srivastava, Pramod Kumar, Mishra, Jyoti, Ojha, Rudra Pratap, Pandey, Purnendu Shekhar, Dwivedi, Radhe Shyam, Carnevale, Lorenzo, Galletta, Antonino
Format: Article
Language:English
Subjects:
Analytical models
Basic Reproduction Number
Complex systems
Cybersecurity
Data collection
Dynamical systems
Epidemics
Epidemiology
Fractional calculus
Fractional Order Derivative
Heterogeneity
Malware
Mathematical analysis
Mathematical models
Nodes
Nonlinear systems
Numerical models
Predictor-corrector methods
Propagation
Sensors
Stability Analysis
System effectiveness
Vaccines
Wireless sensor networks
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Summary:The malware spreading in Wireless Sensor Network (WSN) has lately attracted the attention of many researchers as a hot problem in nonlinear systems. WSN is a collection of sensor nodes that communicate with each other wirelessly. These nodes are linked in a decentralised and distributed structure, allowing for efficient data collection and communication. Due to their decentralised architecture and limited resources, WSN is vulnerable to security risks, including malware attacks. Malware can attack sensor nodes, causing them to malfunction and consume more energy. These attacks can spread from one infected node to others in the network, making it essential to protect WSN against malware attacks. In this paper, we focus on the analysis of a novel fractional epidemiology model, specifically the fractional order SEIVR epidemic model in the sense of Caputo's fractional derivative of order 0
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3276351