aspg-3-3575-1791417695 20261008000135 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 1 Vyom Kulshreshtha PhD Scholar, Computer Science & Engineering, Amity University Madhya PradeshnGwalior, India Deepak Motwani Computer Science Engineering, Amity University Madhya Pradesh, Gwalior, India Pankaj Sharma Computer Science Engineering, Eshan College of Engineering, Mathura, India Ransomware or crypto-ransomware is a big headache to digital media and transactions nowadays. Generally, Ransomware affects the operating system and transfers the valuable information and data stored in the system. Some ransomware attacks the system and corrupts the system file, making it useless to the user. Data encryption with a private key is also one of the attaching fashions of some types of ransomwares. Most ransomware attacks are reported in android operating system-based devices. The solution to ransomware is only the earlier identification of an attacked pattern in the operating system and removal of it. Artificial Intelligence (AI) plays a major role in various kinds of attack detection and classification processes. Machine learning (ML) technique can be used to train and classify the presence of ransomware in android-based devices. Various parameters, such as the characteristics of applications' permission access to various inputs of the devices. The data can be used to train the Recurrent Neural Network (RNN), the most popular and highly accurate ML module that performs a highly accurate classification process. The performance can be evaluated using various sensitivity evaluation metrics such as accuracy, sensitivity, specificity, and precision. 2025 75 83 3575 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190107 https://www.americaspg.com/journal/3/article/3575 https://www.americaspg.com/storage/21739579067.pdf