Cyber Fraud App Detection Using Machine Learning

ABSTRACT

Cyber fraud detection and prevention using machine learning (ML) is an advanced approach to safeguarding digital transactions and data integrity.
Machine learning algorithms, including supervised and unsupervised learning techniques, are leveraged to analyze vast amounts of data, identify patterns, and detect anomalies indicative of fraudulent activities.
By continuously learning from new data, ML models can adapt to evolving cyber threats and provide real-time fraud detection, reducing false positives and enhancing accuracy.
This approach not only improves the efficiency and effectiveness of fraud detection systems but also helps in predicting potential frauds, thereby enabling proactive measures to prevent cyber attacks.

EXISTING SYSTEM

The existing system for cyber fraud detection and prevention often employs Support Vector Machines (SVM), a powerful machine learning algorithm known for its effectiveness in classification tasks.
SVM works by finding the optimal hyperplane that separates different classes of data with maximum margin, making it well-suited for detecting anomalies indicative of fraud.
The system continuously updates with new data, refining its accuracy and adaptability to emerging fraud patterns.
SVM’s robustness to overfitting and its ability to handle high-dimensional data make it a popular choice for cyber fraud detection, contributing to real-time identification and prevention of fraudulent activities.

DISADVANTAGES

While the Support Vector Machine (SVM) algorithm is effective for cyber fraud detection, it has several disadvantages in this application.
One major drawback is its computational complexity, which can lead to slow processing times, particularly with large datasets common in fraud detection.
Furthermore, SVM’s interpretability is limited compared to some other machine learning methods, making it difficult to understand and justify the decision-making process of the model to stakeholders.
Where fraudulent Apps are much rarer than legitimate ones, potentially leading to lower detection rates for fraud

PROPOSED SYSTEM

The proposed system for cyber fraud detection and prevention utilizes Convolutional Neural Networks (CNNs), a deep learning algorithm traditionally used in image processing but increasingly applied to time series and sequential data.
This system leverages CNNs’ ability to automatically extract and learn hierarchical features from raw data, reducing the need for extensive feature engineering.
The proposed system processes transaction data, such as amount, time, location, and user behavior, as multi-dimensional input to the CNN.
The network’s convolutional layers identify complex patterns and correlations within the data, while the fully connected layers classify transactions as either legitimate or fraudulent.

ADVANTAGES

PROJECT DEMO VIDEO

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Hardware Requirements

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