Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
1.1. Background and Motivation
1.2. Blockchain Technology
1.3. Blockchain Challenges
1.4. Attacks on Blockchain
1.5. Aims, Novelty, and Contribution
- A novel deep-learning model for detecting phish scams in blockchain transactions is presented.
- Using Bi-LSTM, CNN-LSTM, and embedded-LSTM on the Ethereum transaction network dataset is demonstrated.
2. Literature Review
2.1. Blockchain Technology and Machine Learning
2.2. Blockchain-Based Approach for IoT
2.3. Fraud Detection on the Blockchain Network
2.4. Phishing Scam-Related Studies in Blockchain Transactions
- Tokens List: This list contains information about different tokens built on the Ethereum blockchain. It includes token names, symbols, addresses, decimals, and additional attributes.
- Contracts List: This list focuses on smart contracts deployed on the Ethereum blockchain. It provides information about the contract address, ABI (application binary interface), and other relevant contract details.
- Addresses List: This list includes Ethereum addresses associated with specific entities or projects. It includes addresses of known wallets, exchanges, dApps (decentralized applications), and other relevant Ethereum participants.
- ENS (Ethereum name service) List: ENS is a decentralized domain name system built on the Ethereum blockchain. This list contains ENS domain names and their corresponding Ethereum addresses.
- Airdrops List: Airdrops refers to the distribution of free tokens to the Ethereum community. This list provides information about past and upcoming airdrops, including details about the airdrop project, token, and distribution methods.
- ENS Reverse Resolution List: This list is a reverse lookup for ENS domain names. It maps Ethereum addresses back to their corresponding ENS domains.
3.2. Proposed Framework
3.3. Word Embedding
3.4. Deep Convolutional Neural Networks
3.4.1. Long Short-Term Memory (LSTM)
3.4.2. Bi-Directional Long Short-Term Memory (Bi-LSTM)
3.5. Parameter Settings
3.6. Ensemble Voting
|Algorithm 1. Ensemble stacking algorithm. Ensemble learning algorithm|
|Input: Training dataset , base learners , meta-learner .|
|Output: Ensemble model predictions.|
|Stage 1: Construct an ensemble of base models:|
|Stage 2: Train the ensemble:|
|Stage 3: Test the meta-learner on new data:|
3.7. Performance Evaluation
4. Experimental Results
4.1. Implementation and Dataset
4.3. Comparison with Existing Methodologies
- The blacklist-based approach involves maintaining a blacklist of known phishing addresses or patterns. Phishing addresses or patterns are added to the blacklist based on historical data or user reports. When a new transaction occurs, it is checked against the blacklist, and if a match is found, the transaction is flagged as potentially malicious. However, this approach relies on the availability and accuracy of the blacklist, which can be challenging to maintain and may not cover all possible phishing attempts.
- The heuristics-based approach utilizes predefined heuristics or rules to identify potential phishing transactions. These heuristics can include unusual transaction patterns, high gas fees, suspicious addresses, or known phishing indicators. Phishing attempts are flagged based on the violation of these heuristics. While heuristics can be effective in detecting some phishing attempts, they may also generate false positives or miss new and evolving phishing techniques.
- The machine-learning (ML)-based approaches leverage algorithms and models trained on historical data to detect phishing attempts. Features such as transaction patterns, transaction metadata, address reputation, and network behavior are extracted, and ML models are trained to classify transactions as phishing or legitimate. ML models, such as decision trees, random forests, or neural networks, can make predictions based on these features. ML-based approaches have the advantage of adapting to new phishing techniques by continuously retraining the models. However, they require a significant amount of labeled training data and may have difficulty handling adversarial attacks aimed at bypassing the detection models.
- The consensus-based approach involves utilizing the consensus mechanism of the blockchain network to detect phishing attempts. By analyzing the behavior of nodes in the network and comparing their transaction validation results, discrepancies or suspicious behavior can be identified. Nodes that consistently provide incorrect validation results or exhibit malicious behavior can be flagged as potential phishing nodes. This approach relies on the assumption that a majority of the network nodes are honest and can be challenging to implement in networks with a low number of participating nodes.
- Ensemble learning can be applied by combining multiple machine learning models, such as decision trees, random forests, or gradient-boosting algorithms. Each model in the ensemble is trained on different subsets of the data or with different feature representations. The outputs of individual models are combined, either through majority voting or weighted averaging, to make the final prediction. Ensemble learning can improve detection accuracy by leveraging the strengths of different models and reducing the impact of individual model weaknesses. It can also help mitigate false positives and false negatives, leading to more reliable phishing detection in blockchain transaction networks.
- Ensemble learning can also be applied by combining different methodologies discussed earlier, such as combining blacklisting, heuristics, and machine learning-based approaches. Each methodology can contribute unique strengths to the ensemble, leading to a more comprehensive and robust phishing detection system. For example, the outputs of blacklisting, heuristics, and machine learning models can be combined using ensemble techniques to make the final decision. This approach helps leverage the complementary nature of different methodologies and enhance the overall accuracy and effectiveness of phishing detection.
- Ensemble learning can be made adaptive by continuously monitoring the performance of individual models or methodologies and dynamically adjusting their contributions to the ensemble. This adaptability allows the system to respond to changes in the phishing landscape and quickly incorporate new detection techniques or update existing models. By combining ensemble learning with adaptive mechanisms, the phishing detection system can stay updated with evolving phishing techniques and improve its resilience against emerging threats in blockchain transaction networks.
4.4. Comparison with Existing Studies
Data Availability Statement
Conflicts of Interest
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|Num of filters (CNN)||16|
|Filter length (CNN)||5|
|Max Sequence Length||32|
|Loss||Binary cross entropy|
|Activation unit||ReLU -Rectified linear units|
|Input features||16, 32, 64||32|
|Hidden size||32, 64, 128, 256||64|
|Number of layers||2, 3, 4||3|
|Batch size||16, 32, 64||32|
|Dropout||0.2, 0.3, 0.4, 0.5, 0.6, 0.7||0.3|
|Learning rate||0.00001, 0.0001, 0.001||0.0001|
|Optimizer||Adam, nAdam, SGD||Adam|
|LSTM||Hidden layers = 128|
Dropout = 0.3
Recurrent dropout = 0.3
|Bidirectional LSTM||Hidden layers = 128|
Dropout = 0.3
Recurrent dropout = 0.3
|CNN + LSTM||Number of convolution filters = 512|
Kernel size = 3
Activation function = RELU
Hidden layers = 128
Dropout = 0.3
Recurrent dropout = 0.3
|||XGBoost||Malicious users’ detection||96.54%||-----||------||-----|
|||Multilayer perception||Cryptocurrency deception||98.00%||------||98.98%||------|
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Ogundokun, R.O.; Arowolo, M.O.; Damaševičius, R.; Misra, S. Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning. Telecom 2023, 4, 279-297. https://doi.org/10.3390/telecom4020017
Ogundokun RO, Arowolo MO, Damaševičius R, Misra S. Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning. Telecom. 2023; 4(2):279-297. https://doi.org/10.3390/telecom4020017Chicago/Turabian Style
Ogundokun, Roseline Oluwaseun, Micheal Olaolu Arowolo, Robertas Damaševičius, and Sanjay Misra. 2023. "Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning" Telecom 4, no. 2: 279-297. https://doi.org/10.3390/telecom4020017