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Security Model to Mitigate Black Hole Attack on Internet of Battlefield Things (IoBT) Using Trust and K-Means Clustering Algorithm


Affiliations
1 Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
 

The Internet of Things (IoT) acts an imperative part in the Battlefield Network (BN) for group-based communication. The new technology is called Internet of Battlefield Things (IoBT) that delivers intelligence services on the battlefield to soldiers and commanders equipped with smart devices. Though it provides numerous benefits, it is also susceptible to many attacks, because of the open and remote deployment of Battlefield Things (BTs). It is more critical to provide security in such networks than in commercial IoT applications because they must contend with both IoT networks and tactical battlefield environments. Because of restricted resources, an attacker may compromise the BTs. The BT that has been seized by the adversary is called a malicious BT and it may launch several security attacks on the BN. To identify these malicious BTs, the IoBT network requires a reputation-based trust model. To address the black hole attack or malicious attack over Routing Protocol for Low Power and Lossy Networks (RPL) is a key objective of the proposed work. The proposed work is the combination of both machine learning algorithm and trust management and it is named as KmCtrust model. By removing malicious BTs from the network, only BTs participating in the mission are trusted, which improves mission performance in the IoBT network. The simulation analysis of KmCtrust model has witnessed the better results in terms of various performance metrics.

Keywords

IoBT, RPL, Trust, Black Hole Attack, Multiple Regression, K-Means Clustering Algorithm, Security.
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  • Security Model to Mitigate Black Hole Attack on Internet of Battlefield Things (IoBT) Using Trust and K-Means Clustering Algorithm

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Authors

P. Rutravigneshwaran
Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
G. Anitha
Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

Abstract


The Internet of Things (IoT) acts an imperative part in the Battlefield Network (BN) for group-based communication. The new technology is called Internet of Battlefield Things (IoBT) that delivers intelligence services on the battlefield to soldiers and commanders equipped with smart devices. Though it provides numerous benefits, it is also susceptible to many attacks, because of the open and remote deployment of Battlefield Things (BTs). It is more critical to provide security in such networks than in commercial IoT applications because they must contend with both IoT networks and tactical battlefield environments. Because of restricted resources, an attacker may compromise the BTs. The BT that has been seized by the adversary is called a malicious BT and it may launch several security attacks on the BN. To identify these malicious BTs, the IoBT network requires a reputation-based trust model. To address the black hole attack or malicious attack over Routing Protocol for Low Power and Lossy Networks (RPL) is a key objective of the proposed work. The proposed work is the combination of both machine learning algorithm and trust management and it is named as KmCtrust model. By removing malicious BTs from the network, only BTs participating in the mission are trusted, which improves mission performance in the IoBT network. The simulation analysis of KmCtrust model has witnessed the better results in terms of various performance metrics.

Keywords


IoBT, RPL, Trust, Black Hole Attack, Multiple Regression, K-Means Clustering Algorithm, Security.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F218514