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Image-Based Scam Detection Method Using an Attention Capsule Network
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DOI：10.1109/ACCESS.2021.3059806.png)
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Abstract
En 中文
In recent years, the rapid development of blockchain technology has attracted much attention from people around the world. Scammers take advantage of the pseudo-anonymity of blockchain to implement financial fraud. The Ponzi scheme, one of the main scam methods, has defrauded investors of large amounts of money, thereby harming their interests and hindering the application of blockchain. Unfortunately, the current detection technology typically largely relies on the source code of the contract or uses a single feature which does not fully represent the contract characteristics. In such a case, the detection of Ponzi schemes with high efficiency becomes urgent. In this paper, we propose an image-based scam detection method using an attention capsule network (SE-CapsNet) focused on Ethereum. The sequence of bytecode, the opcode frequency, and the application binary interface (ABI) call are extracted as features from the contract bytecode and ABI, further converted into grayscale images, and then mapped into three color channels to generate RGB images, which are used as the input of the model for detecting the Ponzi scheme contract. In addition, we employ fancy PCA for data augmentation to reduce the impact of imbalanced data on the detection results. Experimental results show that the image-based detection method using deep learning models can effectively detect contracts before transactions occur. Among them, our proposed SE-CapsNet obtains great detection results, with an F1 score of 98.38%.
Keywords:
Feature extraction
Contracts
Smart contracts
Blockchain
Malware
Task analysis
Gray-scale
Blockchain
capsule network
Ethereum
Ponzi scheme
smart contract
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