Modeling Worm Proliferation in Wireless Sensor Networks with Discrete Fractional Order System

Selvam, A. George Maria; Winster, S. Godfrey; Janagaraj, R.; Jones, G. Maria

doi:10.35940/ijrte.e4594.018520

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Researchers have developed various security mechanisms to protect WSNs against malware attacks [9], [10], [11], [12], [13], [14].These include secure routing protocols, intrusion detection systems, and data encryption techniques. However, the nature of WSNs makes it challenging to implement these security mechanisms effectively, given the limited resources available to the sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

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

Srivastava¹,

Srivastava²,

Mishra³

et al. 2023

IEEE Access

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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< α≤1 with the goal of examining the efficacy of vaccination strategies and the heterogeneity of a scale-free network on epidemic spreading. First, using the next-generation technique and obtain the basic reproduction number of the proposed epidemic model, which is essential for determining both the locally asymptotically stable equilibrium point of the wormfree system and the unique existence of the endemic equilibrium point. To numerically solve the model, the Adam-Bashforth-Moulton predictor-corrector (ABM) method is applied. The fractional calculus enables us to deal directly with the ''memory effect'' of numerous phenomena, taking into account the system's dependence on previous stages. This method provides the results of a complex system. Additionally, research demonstrates that vaccine treatments are quite effective at preventing the spread of malware. The outcome of the study reveals that the applied ABM predictor-corrector method is computationally strong and effective to analyse fractional order dynamical systems in the SEIVR epidemic model for malware propagation in WSN. The results show that the order of the fractional derivative has a significant effect on the dynamic process. Also, from the result, it is obvious that the memory effect is zero for α = 1. When the fractional order α is decreased from 1, the memory effect appears, and its dynamics vary according to the time. This memory effect points out the difference between derivatives of fractional and integer orders. The theorems and their proofs are presented to validate the validity of the proposed model. To validate the proposed model, extensive theoretical study and computational analysis have also been applied.The associate editor coordinating the review of this manuscript and approving it for publication was Stefano Scanzio .

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Section: Introductionmentioning

confidence: 99%

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

Srivastava¹,

Srivastava²,

Mishra³

et al. 2023

IEEE Access

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“…Cabir, a virus that spreads through the air interface, has recently surfaced, causing severe damage to WSNs. As a result, the WSN group is particularly interested in security techniques for defending sensor nodes against software attacks 10–16 . In the epidemiological component of computer virus/worm propagation over networks, mathematical modeling acts as a critical tool in these situations.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the WSN group is particularly interested in security techniques for defending sensor nodes against software attacks. [10][11][12][13][14][15][16] In the epidemiological component of computer virus/worm propagation over networks, mathematical modeling acts as a critical tool in these situations. The transmission patterns of worms from one node to the next can be better understood and monitored via mathematical models of propagation.…”

mentioning

confidence: 99%

Dynamics of the worm transmission in wireless sensor network in the framework of fractional derivatives

Achar

Baishya

Kaabar

2021

Math Methods in App Sciences

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Wireless sensor networks (WSNs) are subject to cyber attacks. Security of such networks is a significant priority for everyone. Due to the network's frail defense mechanisms, WSNs are easy targets for worm attacks. A single unsecured node via contact can effectively propagate the worm across the entire network. Mathematical epidemic models are helpful in analyzing worm propagation in WSNs. To understand the attacking and spreading dynamics of worms in WSNs, a fractional‐order compartmental epidemic model is investigated with susceptible, exposed, infected, recovered, and vaccinated nodes. Dynamical aspects such as boundedness, existence, and uniqueness of the solutions are presented with the help of fractional calculus theory. Global stability of the points of equilibrium are established. The projected nonlinear structure is examined numerically via the generalized Adams–Bashforth–Moulton method. This study demonstrates the influence of the fractional operator on WSNs dynamics and the efficiency of the numerical method.

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Modeling Worm Proliferation in Wireless Sensor Networks with Discrete Fractional Order System

Cited by 2 publications

References 8 publications

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

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

Dynamics of the worm transmission in wireless sensor network in the framework of fractional derivatives

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