Analysis of Ransomware Impact on Android Systems using Machine Learning Techniques

Abstract

Ransomware is a significant threat to Android systems. Traditional methods of detection and prediction have been used, but with the advancement of technology and artificial intelligence, new and innovative techniques have been developed. Machine learning (ML) algorithms are a branch of artificial intelligence that have several important advantages, including phishing detection, malware detection, and spam filtering. ML algorithms can also be used to detect ransomware by learning the patterns and behaviors associated with ransomware attacks. ML algorithms can be used to develop detection systems that are more effective than traditional signature-based methods. The selection of the dataset is a crucial step in developing an ML-based ransomware detection system. The dataset should be large, diverse, and representative of the real-world threats that the system will face. It should also include a variety of features that are informative for ransomware detection. This research presents a survey of ML algorithms for ransomware detection and prediction. The authors discuss the advantages of ML-based ransomware detection systems over traditional signature-based methods. They also discuss the importance of selecting a large, diverse, and representative dataset for training ML algorithms. Two datasets are applied during the conducted experiments, which are SEL and ransomware datasets. The experiments are repeated with different splitting ratios to identify the overall performance of each ML algorithm. The results of the paper are also compared to recent methods of ransomware detection and showed high performance of the proposed model.