# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 4 Experimental Results
### Comparison with Existing Methodologies  
Phishing detection in blockchain transaction networks is a crucial task to protect users from malicious activities and ensure the security of their transactions. There are several methodologies and approaches that can be used to detect phishing attempts in blockchain transaction networks. Let's discuss and compare some of these 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. The effectiveness of each methodology can vary depending on the specific characteristics of the blockchain network, the nature of phishing attempts, and the available resources. Combining these methodologies, such as machine learning for initial detection and heuristics-based rules for rule-based validation, can provide a more robust phishing detection system. Regular updates, continuous monitoring, and collaboration between researchers, developers, and users are essential to staying ahead of emerging phishing techniques in blockchain transaction networks. Ensemble learning is a powerful methodology that combines multiple models to improve the overall performance and robustness of a system. Applying ensemble learning to phishing detection in blockchain transaction networks can enhance the accuracy and effectiveness of the detection process. We compare ensemble learning-based methodologies with the previously discussed methodologies for phishing detection in blockchain transaction networks as follows:  
* 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.