The document discusses an ensemble deep learning approach for predicting fraudulent cryptocurrency transactions by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) models. The proposed system, which employs a bagged LSTM model, achieves a significant accuracy of 96.4% in detecting fraudulent activities, leveraging a dataset of 395 million transactions. Challenges and benefits of the ensemble approach, as well as software specifications for implementation, are outlined.

1. Base paper Title: Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency
Transactions
Modified Title: Prediction of Fraudulent Cryptocurrency Transactions using Ensemble Deep
Learning
Abstract
Cryptocurrency has emerged as a decentralized transaction to overcome the problems
of the centralized transaction system. Although it has become a popular trend in online
cryptocurrency transactions and mobile wallets, this method has increased the number of
fraudulent transactions instead of physically transferring money. Because the shared data and
the history of online transactions may lead to fraudulent transactions. The preprocess
identification of fraudulent cryptocurrency transactions is becoming an urgent research
question. With the exponential blossoming of Artificial Intelligence, the employing of deep
learning in predicting social issues has been achieved in many disciplines. From this
perspective, this paper proposes an ensemble learning approach for fraudulent cryptocurrency
transactions by integrating two deep learning methods: Convolutional Neural Network (CNN)
and Long Short-Term Memory (LSTM). The off-theshelf CNN and LSTM, ensemble CNN,
and ensemble LSTM with the bagged and boosted approach are compared in terms of accuracy
and losses from training and test datasets. Moreover, the 10-fold crossvalidation approach is
employed for the evaluation of the proposed approach. The evaluation results indicate that the
bagged LSTM ensembled approach is significant with 96.4% accuracy and outperforms the
other approaches.
Existing System
Cryptocurrency has emerged as an exciting platform with the potential to overcome
problems associated with the existing modes of payments and transactions [1]. The tremendous
increase in the use of cryptocurrency in the payment area has not only unlocked more
opportunities and challenges but involved criminal activities [2], [3], [4]. According to one
estimate, a thousand cryptocurrencies enter the market each month with different usability [5].
Moreover, more than 12000 cryptocurrencies will be available by 2022, and 70 of these
cryptocurrencies have a market cap of more than one billion dollars [5]. The top ten
cryptocurrencies by market cap are shown in Table 1. Blockchains are used for the

2. development of cryptocurrencies [6] that maintain public ledgers for managing cryptocurrency
transactions [7]. Cryptocurrency transactions are decentralized and recorded in a peer-to-peer
network called a blockchain [8], [9], eliminating the need for a central authority [10]. Bitcoin
is a pioneering cryptocurrency, but there are also many other coins with significant potential.
For example, Ethereum is the second-largest cryptocurrency by market capitalization [6] that
allows smart contracts [11]. An overview of Ethereum’s value is presented in Fig. 1. The
Bitcoin network has a limited capacity to handle many transactions quickly; therefore, it is not
scalable [10]. Ethereum reduces the problem of the scalability of Bitcoin [10]. Vitalik Buterin
develops Ethereum to delegate power to the user [10]. The main advantage of Ethereum is its
low transaction fee (gas) [12] required to execute a transaction on Ethereum, regardless of
transaction success or failure. Each gwei (Ethereum gas unit) is equal to 0.000000001 ETH
(10−9 ETH). Ethereum is more adaptable to smart contracts and transactions [1]. Smart
contracts are a type of Ethereum account. This means they have a balance and can be the target
of transactions. Therefore, fraudulent transactions may occur through smart contracts. The
decentralized blockchain approach allows operations without central control and
intermediaries with several benefits related to privacy and security [14], e.g., transaction
anonymity [15]. Such benefits make fraudulent behavior very common [16], i.e., the
decentralized control of blockchain and transactions’ anonymity leads to frequent fraudulent
transaction behavior in cryptocurrency [17], [18].
Drawback in Existing System
 Interpretability: Ensemble models are generally more complex than individual
models, making it challenging to interpret and understand the decision-making process.
The lack of interpretability can be a significant drawback, especially in applications
where regulatory compliance or user trust is crucial. Way More Time Spent Not
Shooting.
 Computational Complexity: Ensemble methods typically involve training and
maintaining multiple deep learning models, which can be computationally intensive
and require significant resources. This complexity can make it challenging to deploy
the model in real-time or resource-constrained environments.

3.  Resource Consumption: Running multiple deep learning models simultaneously can
consume significant computational resources, leading to higher operational costs,
especially if deployed on cloud platforms.
Proposed System
 Proposed supervised machine learning based anomaly and criminal activity detection
in a Bitcoin based eco system. The proposed solution is based on a dataset of 395
million transactions with 957 unique clusters.
 Proposed machine learning-based abnormal activity detection from suspicious users in
Ethereum platforms. The study also compares the performance of the different
machine-learning algorithms.
 Proposed the temporal debiasing method by using Graph Neural Network (GNN)
approach for fraud detection in cryptocurrency.
 Proposed an exploratory simulation model to detect the anomaly in the Bitcoin system.
The proposed simulation model helps to reduce double-spending risks with absolute
accuracy. Monamo et al. [38] recommended Unsupervised Anomaly Detection, which
finds outliers in trends using trimmed k-means.
Algorithm
 Bagging (Bootstrap Aggregating):
Random Forest: A popular bagging algorithm that builds multiple decision trees and
combines their predictions. Each tree is trained on a random subset of the data.
Bagged Neural Networks: Applies bagging to neural networks by training multiple
networks on different subsets of the training data.
 Stacking:
Stacked Generalization: Combines the predictions of multiple models by training a
meta-model (or blender) on their outputs. The base models serve as input features for
the meta-model.
Feature Stacking: Concatenates the outputs of multiple models along with the original
features, creating an extended feature set for training the final model.

4.  AutoML-Based Ensembles:
Automated Machine Learning (AutoML): Platforms like AutoML can automatically
search and select the best combination of models, hyperparameters, and ensemble
methods for a given task.
Advantages
 Improved Accuracy: Ensemble models often outperform individual models by
leveraging the diversity of multiple base models. This can lead to improved accuracy
in identifying fraudulent transactions, as the ensemble can capture a wider range of
patterns and features.
 Handling Complexity: Crypto currency transactions datasets can be complex, with
various types of fraud and constantly evolving patterns. Ensemble models can handle
this complexity more effectively than single models, providing a more comprehensive
and adaptive approach to fraud detection.
 Flexibility: Ensemble models are flexible and can incorporate different types of base
models, such as neural networks, decision trees, or support vector machines. This
flexibility enables practitioners to choose models that are well-suited to the specific
characteristics of the cryptocurrency transaction data.
 Model Interpretability: While ensemble models can be more complex, techniques
such as feature importance analysis or model-agnostic interpretability methods can help
provide insights into the contributions of different features and models, enhancing
interpretability compared to using a single complex model.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB

5. Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm