International Journal of Computer Networks and Applications (IJCNA)

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ISSN : 2395-0455

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

A Novel LPRPO-LSTDCNN Based Side Channel Attack Detection and Secure Data Transmission Framework Using DH-ATM-RFICC

Author NameAuthor Details

Prasath Vijayan, Sudalaimuthu T

Prasath Vijayan[1]

Sudalaimuthu T[2]

[1]Department of Computer Science and Engineering, Perunthalaivar Kamarajar Institute of Engineering and Technology, Karaikal, India.

[2]School of Engineering and Technology, Hindustan Institute of Technology and Science (Deemed to be University), Chennai, India.

Cite this Article

DOI: 10.22247/ijcna/2024/48

Pages : 803-820

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Abstract

Side Channel Attack (SCA) is the exploitation of data security due to information leakage from a device. However, the existing studies didn’t focus on the detection of SCA based on its types and the prevailing works had security leakage and time complexities. Therefore, the paper presents the SCA detection with multi-class classification for secured data transmission. First, the device is installed and the public and private keys are generated. Next, Universally Unique Identifier (UUID) is generated and based on the public key and UUID, a hash code is generated using Interpolation Harmonic Entropy based SWIFFT(IHE-SWIFFT) hashing algorithm. At the same time, a secret key is generated using a public key and UUID. Then, with the help of a secret key and public key, data is encrypted using Diffie-Hellman Asymmetric Tent map-based Robust Frobenius Isogenies Curve Cryptography (DH-ATM-RFICC). For the purpose of user authorization, hash code matching is carried out. If the hash code is not matched, then the transaction will be declined to prevent from unauthorized transactions. If the hash code at the time of transaction initialization is matched with the hash code generated during the transaction, then the data is gathered by extracting the features using Gini Point Bi-serial Correlation-based Empirical Wavelet Transform (GPBC-EWT).The extracted features are then reduced using Bag of Deep Features (BoDF) and through hybrid classifier to detect the SCAs using Lagrange Polynomial Red Panda Optimization with Logistic Softmax Tree Hierarchical Deep Convolutional Neural Network (LPRPO-LSTDCNN). Finally, the data under no attack are decrypted in the server for successful transaction. Hence, the proposed model detected the attack with 98.87% Accuracy, 98.86% Precision, 98.88% Recall, and decrypted the data securely in 978ms, thus showing better performance than existing models.

Index Terms

Side Channel Attack

Deep Learning

Cryptography

Deep Convolutional Neural Network

Red Panda Optimization

Hashing algorithm

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