Ransomware Detection with Machine Learning by Applying the Lapranove Function on Bytecode

In this study, we made the novel application of the Lapranove function to ransomware detection, offering a new approach to enhancing detection accuracy and reliability. The Lapranove function markedly improves feature extraction, thus boosting machine learning models' performance in identifying ransomware. This research rigorously evaluates the effectiveness of the Lapranove function-enhanced feature set using Support Vector Machines, Random Forests, and Neural Networks. Experimental results demonstrate that the Neural Network model substantially outperforms traditional detection methods, achieving superior metrics in accuracy, precision, recall, and F1 score. Detailed analysis of the experimental setup, results, and comparative performance with baseline methods highlights the considerable benefits of integrating advanced mathematical functions with state-of-the-art machine learning techniques. The findings emphasize the potential of this innovative approach to enhance cybersecurity systems' accuracy and reliability, providing a robust framework adaptable to other malware and cybersecurity threats. This study shows the importance of advanced feature engineering and high-quality datasets in training effective machine learning models, paving the way for future research and practical applications in enhancing malware detection capabilities.