## 2 Literature Review
### Deep-Learning Approaches  
Phishing attacks have been the subject of numerous research works over the last few years. [PERSON] et al. (2018) surveyed several deep-learning-based methods for phishing detection in a broader landscape. In this subsection, a description of deep-learning-based methods for phishing attack detection in the blockchain is provided.  
[PERSON] et al. (2019) presented a deep-learning model to classify phishing emails and then present the results to human evaluators for validation. The feedback was used to improve the accuracy of the model over time. The approach has been shown to be effective in detecting both known and unknown phishing attacks. The deep-learning model used to detect phishing attacks is a combination of various machine-learning algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are trained on large datasets of known phishing emails and websites to identify patterns that indicate potential threats. The specific model used depends on the type of attack being detected, as well as the available resources and expertise. [PERSON] et al. (2019) proposed a high-performance model for detecting phishing fraud in blockchain cryptocurrency transactions using graph convolutional networks. The model divided the transaction graph into \"sender\" and \"receiver\" graphs and utilized a double-layer graph convolution network for feature learning. Testing on Ethereum data achieved an accuracy of 88.02% and an F1 score of 88.14%, outperforming other models and introducing a new concept for detecting phishing scams in blockchain networks.  
[PERSON] et al. (2018) addressed the privacy vulnerability in a blockchain-based smart healthcare system where personal information is exposed. They presented an attack scenario that infers user identity by analyzing the blockchain graph and constructing account-transaction graphs. Graph-embedding algorithms and machine-learning techniques are used to identify participants, achieving an inference performance of up to 0.94 in the F1 score when tested on Ethereum. [PERSON] et al. (2018) addressed security and latency issues in blockchain-basedhealthcare systems and proposed a hybrid deep-learning-based homomorphic encryption (HE) model for the Industrial Internet of Medical Things (IIoMT). The model integrates HE with a consortium blockchain to provide privacy, resistance to attacks, and the ability to perform statistical and machine-learning operations on encrypted EMR data. Comparative simulation analysis demonstrated enhanced security, efficiency, and transparency compared to existing approaches. In addition to the aforementioned techniques, [PERSON] et al. [19] introduced a deep-learning-based fraud detection model using Ethereum blockchain transaction data. The model relies on long short-term memory (LSTM) and dense units, with feature selection using information gain. Conversely, [PERSON] et al. [20] designed a secure, intelligent fuzzy blockchain framework that incorporates a fuzzy deep-learning model and fuzzy control systems for network attack detection. The framework employs metaheuristic algorithms for optimization and fuzzy matching for fraud detection. Evaluation results demonstrate the efficiency and effectiveness of the framework in threat detection and decision-making in blockchain-based IoT networks.