TY - GEN
T1 - Phishing detection in Blockchain Transactions with BEART and Deep CNN Model
AU - Gaurav, Akshat
AU - Gupta, Brij B.
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Phishing attacks in blockchain transactions are a big problem for cryptocurrency exchanges. This paper presents a deep learning framework based on a Blockchain-Enhanced Attention Retention Transformer (BEART) with a deep CNN to find and classify phishing activities in blockchain transactions. To test our model, we used the Kaggle dataset. Our proposed framework also used a feature engineering technique to extract the most relevant features. Finally, our proposed framework gives a high accuracy of 96.24% in spotting phishing transactions.
AB - Phishing attacks in blockchain transactions are a big problem for cryptocurrency exchanges. This paper presents a deep learning framework based on a Blockchain-Enhanced Attention Retention Transformer (BEART) with a deep CNN to find and classify phishing activities in blockchain transactions. To test our model, we used the Kaggle dataset. Our proposed framework also used a feature engineering technique to extract the most relevant features. Finally, our proposed framework gives a high accuracy of 96.24% in spotting phishing transactions.
KW - Blockchain Transactions
KW - Deep Convolutional Neural Networks
KW - Phishing Detection
KW - SMOTE
UR - https://www.scopus.com/pages/publications/85205832839
U2 - 10.1109/CONECCT62155.2024.10677044
DO - 10.1109/CONECCT62155.2024.10677044
M3 - Conference contribution
AN - SCOPUS:85205832839
T3 - Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies
BT - Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies
T2 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2024
Y2 - 12 July 2024 through 14 July 2024
ER -