Analysis of Ransomware Impact on Android Systems using Machine Learning Techniques

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 MLbased 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.