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@article{171417,
        author = {Koyagura Asish and Dr. S. Suhasini},
        title = {Fraudulent Transactions Detection in Bitcoin Network using ML & DL Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {3943-3949},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171417},
        abstract = {With the advent of digital transactions, the use of crypto currency has become an important part in the domain of banking. This also led to the development of various methods to conduct fraudulent transactions which led to many scams. Bitcoin crypto currency is the first crypto currency to be introduced to the world. Even the bitcoin network built based upon blockchain technology is not free from these frauds. Hence, there is a need to take measures against these cries. This work researches the use of machine learning algorithms such as Logistic Regression (LR), Random Forests (RF), Multilayer Perceptron (MLP), XGBoost (XGB), Long-Short Term Memory (LSTM) & Convolution Neural Network (CNN) to detect these fraudulent transactions. It is based on data of the transactions details and wallet actors that are present in the Bitcoin network.  It uses the transaction dataset & actor’s dataset to train machine learning models based on different algorithms then compare their perfromance. This machine model and methodology can be expected to be useful for different cryptocurrencies such as ZCash etc.},
        keywords = {Transactions, Actors, Bitcoin, Fraud, Machine learning.},
        month = {January},
        }