A Time Interval based Blockchain Model for Detection of Malicious Nodes in MANET Using Network Block Monitoring Node

Narayana, V. Lakshman; Midhunchakkaravarthy, Divya

doi:10.1109/icirca48905.2020.9183256

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…However, after a node is categorized as malicious, the non-malicious behavior of that node is also ignored, lacking the subjectivity and dynamic timeliness of the judgment. Narayana & Midhunchakkaravarthy (2020) (ICIRCA-2020) describes a trust assessment and block analysis-based model for the detection of malicious nodes in mobile ad hoc networks. Malicious nodes in the network are detected by analyzing the blocks generated by each node after transmitting data.…”

Section: Related Workmentioning

confidence: 99%

Detection of malicious nodes based on consortium blockchain

Luo,

Lai,

et al. 2024

PeerJ Computer Science

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With the development of technology, more and more devices are connected to the Internet. According to statistics, Internet of Things (IoT) devices have reached tens of billions of units, which forms a massive Internet of Things system. Social Internet of Things (SIoT) is an essential extension of the IoT system. Because of the heterogeneity present in the SIoT system and the limited resources available, it is facing increasing security issues, which hinders the interaction of SIoT information. Consortium chain combined with the trust problem in SIoT systems has gradually become an important goal to improve the security of SIoT data interaction. Detection of malicious nodes is one of the key points to solve the trust problem. In this article, we focus on the consortium chain network. According to the information characteristics of nodes on the consortium chain, it can be analyzed that the SIoT malicious node detection combined with the consortium chain network should have the privacy protection, subjectivity, uncertainty, lightweight, dynamic timeliness and so on. In response to the features above and the concerns of existing malicious node detection methods, we propose an algorithm based on inter-block delay. We employ unsupervised clustering algorithms, including K-means and DBSCAN, to analyze and compare the data set intercepted from the consortium chain. The results indicate that DBSCAN exhibits the best clustering performance. Finally, we transmit the acquired data onto the chain. We conclude that the proposed algorithm is highly effective in detecting malicious nodes on the combination of SIoT and consortium chain networks.

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Section: Related Workmentioning

confidence: 99%

Detection of malicious nodes based on consortium blockchain

Luo,

Lai,

et al. 2024

PeerJ Computer Science

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“…The current mobile nodes highly necessitate the secure communication. The key goal of this secure communication is to facilitate Quality of Service (QoS) and reliable data transmission in mobile ad-hoc network (MANET) [1,2]. The secure communication in MANET can be established by the combination of trusted mobile nodes and trusted third party.…”

Section: Introductionmentioning

confidence: 99%

“…Authentication is a main process in MANET security that verifies the provided credentials during registration process [6]. Few works [1,2,7] have performed authentication with an aid of blockchain technology to overcome failure during centralized administration when group of attackers combined to target it. Here, the transactions/blocks signature process is performed to provide security in MANET.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of BDAG Aided Blockchain Technology in Clustered Mobile Ad-Hoc Network for Secure Data Transmission

Harikrishnan¹,

Balasubaramanian²

2021

IJACSA

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In mobile ad-hoc network (MANET) environment, routing of data packets is a challenging task due to rapid changes in mobility and network topology. In addition, the security aspect of routing is disturbed by attacks caused by malicious nodes. These attacks greatly affect the Quality of Service. To overcome the challenges faced in routing message packets, the Bayesian Directed Acyclic Graph (B DAG) Aided Blockchain model is proposed for clustered MANET environment. The proposed model encompasses the following processes: (i) Multi factor authentication of users by using BLISS algorithm. This step involves acquisition of user credentials and generates hash values for those credentials using Cube Hash algorithm. These hash values are further used to generate public and private keys by BLISS algorithm, (ii) Weighted sum computation for clustering to reduce complexity in the MANET environment. Cluster head (CH) and cluster member (CM) are classified based on energy status, geometric distance, link quality and direction, (iii) A secure AODV based routing protocol using Dolphin Swarm Optimization (DSO) algorithm. This step involves selection of reputed node based on link stability, relative velocity, available bandwidth, energy, queue length, and trust. The packet forwarding is based on the reputation value of the node, by which the trust provided by malicious nodes are eliminated to improve security and (iv) Bayesian DAG aided blockchain, in which the user authenticity, data integrity of packets and signature are verified to mitigate the routing attacks created by nodes in the MANET environment. The proposed model is experimented in NS 3.26 network simulator tool and its performance in terms of multiple QoS metrics is evaluated.

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