Malicious Node Detection in Wireless Sensor Networks Using Support Vector Machine

Jaint, Bhavnesh; Indu, S.; Pandey, Neeta; Pahwa, Khushbu

doi:10.1109/rdcape47089.2019.8979125

Cited by 11 publications

(2 citation statements)

References 12 publications

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“…It is only suitable for static attack. Therefore, there is a need for the design of an ICNN-MND protocol that is efficient in terms of accuracy, computational cost and suitable for dynamic attacks [4]. Since sensitive data like medical data are continuously being transmitted through the IoT nodes, there is a greater requirement for the preservation of network security in medical applications [5].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNN-MND) Protocol For Internet of Things

Moka

Serome

Rajalakshmi

2023

Preprint

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At present Internet of Things (IoT) is widely used in various products and parts in our daily life. In the future, its uses and requirements will increase. However, there are several issues with the IoT network, one of which is secure data transmission. Also, malicious nodes in the IoT network are very difficult to identify accurately and quickly, whereas it is very important to do so. In the current context, several solutions are being proposed as there is a need to focus on setting up secure IoT frameworks, and as well as the protocols that runs on them. This research proposes a secure protocol called iterative Convolutional Neural Network based Malicious Node Detection (ICNN-MND) protocol that uses the erroneous assumption learning problematic technique. This protocol will make changes in the application layer, the network layer and the physical layer in order to achieve secure data transmission in IoT. The proposed IC-MND protocol will be able to detect attacks and eliminate them hence providing maximum data accuracy in IoT. For this, machine learning method will be used and the changes in the characteristics of the malicious nodes will be learned and their boundaries be explored. Then its error variation will be calculated and the malicious nodes be differentiated.

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Section: Introductionmentioning

confidence: 99%

An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNN-MND) Protocol For Internet of Things

Moka

Serome

Rajalakshmi

2023

Preprint

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“…Specifically, Cui et al [ 15 ] propose a mobile malware detection systems. Jaint et al [ 16 ] and Thaile et al [ 17 ] apply support a vector machine and nodetrust scheme in order to improve the detection efficiency. Once the malware is detected, then a mitigation mechanism, such as dismissing the affected nodes [ 18 ] or adopting diverse variants deployments [ 19 ], would be activated.…”

Section: Introductionmentioning

confidence: 99%

A Novel Epidemic Model for Wireless Rechargeable Sensor Network Security

Liu

Peng

Zhong

2020

Sensors

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With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging and reinfection process of WRSNs is proposed. Subsequently, the local and global stabilities of disease-free and epidemic equilibrium points are analyzed and simulated after obtaining the basic reproductive number R0. Detailedly, the simulations further reveal the unique characteristics of SILS when it tends to being stable, and the relationship between the charging rate and R0. Furthermore, the attack-defense game between malware and WRSNs is constructed and the optimal strategies of both players are obtained. Consequently, in the case of R0<1 and R0>1, the validity of the optimal strategies is verified by comparing with the non-optimal control group in the evolution of sensor nodes and accumulated cost.

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An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNNMND) Protocol For Internet of Things

Moka,

Serome,

Selvaraj

2024

Wireless Pers Commun

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Malicious Node Detection in Wireless Sensor Networks Using Support Vector Machine

Cited by 11 publications

References 12 publications

An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNN-MND) Protocol For Internet of Things

An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNN-MND) Protocol For Internet of Things

A Novel Epidemic Model for Wireless Rechargeable Sensor Network Security

An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNNMND) Protocol For Internet of Things

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