Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract

Aziz, Rabia Musheer; Mahto, Rajul; Goel, Kartik; Das, Aryan; Kumar, Pavan; Saxena, Akash

doi:10.3390/app13020697

Cited by 54 publications

(12 citation statements)

References 27 publications

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“…Slightly differently, a study enhanced LSTMs with an attention mechanism, improving the analysis of Ethereum transaction dynamics [7]. Another study integrated a genetic algorithm with Cuckoo Search to avoid local optima, enhancing scam node detection [8]. A study focusing on wash trades, a specific type of fraud in Ethereum, used a heuristic algorithm to detect scam nodes, highlighting the adaptability and effectiveness of traditional methodologies to address specific types of cryptocurrency fraud [9].…”

Section: Related Workmentioning

confidence: 99%

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Choi,

Buu

2024

Electronics

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Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.

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Section: Related Workmentioning

confidence: 99%

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Choi,

Buu

2024

Electronics

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“…The solution for Equation (1) can be analyzed by equating M : V → R 2 (ρ) with ρ = ∂R, where ∂ represents the partial differential equation with respect to ∂p, ∂v i.e., ∂R ∂p∂v . Therefore, it is understood that there exists a weak sense between MPP and its boundary [25,26]. This can be analyzed by considering N-R-type boundary control and Robin-type boundary control.…”

Section: Problem Formulationmentioning

confidence: 99%

Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique

Jena¹,

Dash

Reddy

et al. 2023

Sustainability

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Maximum Power Point Tracking (MPPT) is a technique used in photovoltaic (PV) systems to maximize the power output from the solar panel by constantly tracking and adjusting the optimal operating point. To achieve this, various algorithms have been developed, with Particle Swarm Optimization (PSO) being a widely used method. By adjusting the control system’s parameters, PSO can determine the optimal operating point of the solar panel and improve its overall performance. PSO employs swarm intelligence by simulating the behavior of particles to find the best solution for a given problem. Long Short-Term Memory (LSTM) belongs to the family of Recurrent Neural Networks (RNN) in machine learning and is designed to address the limitations of traditional RNNs in capturing long-term dependencies that exist in sequential data. The combination of PSO and LSTM techniques can result in an efficient MPPT algorithm that leverages the benefits of both. PSO is utilized to optimize the control parameters of the MPPT algorithm, while LSTM is used to predict the solar panel’s power output based on historical data. Consequently, this integration can lead to an accurate and efficient MPPT algorithm that can effectively track the solar panel’s maximum power point. In this research article, an effort has been made to control the duty cycle of the converter by suitably controlling the system gain. A Matlab-based Simulink model in conjunction with Python programming has been used to make the system more robust.

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“…(1) Classification: Classification is a process that involves assigning a class label to the input data [13,14]. Mostly, a classification task allocates a single label to the input.…”

Section: Deep-learning-based Analysis Tasksmentioning

confidence: 99%

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

et al. 2023

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The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy, provides significant benefits in clinical practice by improving the efficiency and accuracy of diagnosis. Deep learning techniques show remarkable success in analyzing these videos by automating tasks such as classification, detection, and segmentation. In this paper, we review the application of deep learning techniques for analyzing medical diagnostic videos, with a focus on ultrasound and endoscopy. The methodology for selecting the papers consists of two major steps. First, we selected around 350 papers based on the relevance of their titles to our topic. Second, we chose the research articles that focus on deep learning and medical diagnostic videos based on our inclusion and exclusion criteria. We found that convolutional neural networks (CNNs) and long short-term memory (LSTM) are the two most commonly used models that achieve good results in analyzing different types of medical videos. We also found various limitations and open challenges. We highlight the limitations and open challenges in this field, such as labeling and preprocessing of medical videos, class imbalance, and time complexity, as well as incorporating expert knowledge, k-shot learning, live feedback from experts, and medical history with video data. Our review can encourage collaborative research with domain experts and patients to improve the diagnosis of diseases from medical videos.

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Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract

Cited by 54 publications

References 27 publications

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

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