Proposed Model for Credit Card Fraud Detection Model Using Machine Learning Technique

Harith Safwan Ezzulddin

The online payment system is at high risk due to the increasing rates of credit card theft. The primary objective is to identify cases of credit card theft by analysing the purchase history of cardholders and categorising them accordingly. These include an increase in the slope of logistics, steep slope, and scattered woodlands. The proposed model utilises tools such as logistic regression and random forest as machine learning techniques. Additionally, a set of preprocessing techniques is employed, including data balancing using SMOTE. After being trained on a large dataset of credit card transactions, the model is used to detect trends and anomalies that may indicate fraudulent activity, taking into account factors such as transaction amount, location, and time of day. We have used artificial minority oversampling to put the data set into proper perspective. The two algorithms were applied, yielding 97.34% accuracy for Logistic Regression and 99.99% accuracy for Random Forest. The accuracy metric is used for performance evaluation. The results indicate a promising performance that can enhance credit card security, potentially helping to reduce financial losses to victims of fraud.

An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction

Hussein Alrammahi · Mohammed Thakir Mahmood

Intrusion Detection Systems (IDS) are elementary building blocks of network security that can be used to detect unauthorized access and malicious activity. But traditional IDS approaches often suffer from problems such as high false positives, inability to adapt quickly to new threats, and scalability. This paper presents an advanced intrusion detection model that uses machine learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to enhance detection. Using the KDD Cup 1999 data, the framework was highly preprocessed, feature engineered, and hyperparameters adjusted to achieve optimal performance. The Neural Network model outperformed other algorithms at 92.5% accuracy, 93.8% recall, and 92.4% F1-score, proving its ability to identify complex attack patterns with minimal false positives effectively. Additionally, the proposed framework reflected significant improvement over existing IDS solutions that always achieve accuracies of 80–85%. Intrusion Detection Systems (IDS) are important components of security, assuming the task of monitoring, detecting, and responding to unauthorized activities in network frameworks. This work's most notable contributions are its integration of sophisticated machine learning methods, systematic assessment of detection performance on a wide range of attack types, and comparison with well-established IDS benchmarks. In spite of facing issues like the complexity of the dataset and computational requirements, findings point to the efficacy of machine learning-based IDS in countering modern-day cybersecurity threats. Real-time data fusion and improving model interpretability for real-world implementation are areas that need to be addressed in the future.