Detection and Prevention of Black Hole Attack using Tree Hierarchical Deep Convolutional Neural Network and Enhanced Identity-based Encryption in Vehicular Ad Hoc Network

K. Lakshmi Narayanan; R. Naresh

doi:10.5573/IEIESPC.2024.13.1.41

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IEIE Transactions on Smart Processing & Computing 학술저널
IEIE Transactions on Smart Processing & Computing Vol.13 No.1
2024.2 41 - 50 (10page)
DOI : 10.5573/IEIESPC.2024.13.1.41

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K. Lakshmi Narayanan (SRM Institute of Science and Technology)
R. Naresh (SRM Institute of Science and Technology)

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A method for the recognition and prevention of a black hole attack is proposed using a tree hierarchical deep convolutional neural network (THDCNN)and enhanced identity based encryption in a vehicular ad hoc network (VANET). Automobiles are organized with a cluster formula using an improved k-means clustering algorithm. Following the cluster formation, cluster head (CH) selection is done using a balancing composite motion optimization (BCMO) algorithm. After selecting the cluster head, the entrance of a spiteful node occurs in the cluster. The THDCNN is proposed for classifying a cluster node as i) a black hole attack node or ii) a normal node. If a black hole attack node is found, the attack node information is communicated to an individual CH, which makes a final decision. Otherwise, the standard node data is encrypted with the enhanced identity-based encryption algorithm. Finally, the proposed method prevents the normal node data from the attacker. Thus, the proposed method attains higher accuracy and lower computational time than other methods.

#Tree hierarchical deep convolutional neural network #Improved k-means clustering algorithm #Enhanced identity-based encryption algorithm #Black hole attack