Real-time ransomware defense using AI: evaluation and performance metrics

Ransomware attacks are a new and current threat that poses a great threat to any organization or person as it may result in large losses. This paper develops an AI-based ransomware reduction to detect and prevent ransomware activities with relative accuracy in real time. The system employs the use of complex algorithms; the system boosts detection accuracy by 96.858%. The efficiency of the proposed model was also asserted through accuracy, precision, recall F1-score, scalability, and detection tests. The system displayed increased vulnerability in terms of detection efficiency with an accuracy of 98.3%, precision of 96.5%, recall of 97.3%, and F1-score of 94.5% which exceeded ordinary detection paradigms. Such outcomes confirm the presence of the system’s capability to detect ransomware with minimal false alarm rates. Moreover, such outcomes present in the system were tested and demonstrated in a ransomware attack simulation, where the attack was prevented, and data from being encrypted, making the system relevant and efficient in real life. This AI application can be seen as a dependable approach to protecting organizational data from constantly developing ransomware threats while keeping the costs of potential threats to a minimum.