Year: 2025
Type: article
Abstract: As wireless communication has advanced, there are several online security risks. The ransomware prediction system assists in identifying system threats and malware detection. In the past, a variety of machine learning (ML) techniques have been used to try to improve the accuracy of the ransom malware system and the outcomes of malware detection. Using the random forest classification technique and principal component analysis (PCA), this research has suggested a method for creating an effective ransomware system. Whereas the random forest will aid in classification, the PCA will assist in organizing the dataset by lowering its dimensionality. According to the results, the suggested method outperforms other methods like SVM, Naïve Bayes, and Decision Trees in terms of accuracy. Performance time (min) is 3.24 minutes, accuracy rate (%) is 96.78%, and error rate (%) is 0.21% according to the results of the suggested approach
Authors: Kanakam Maruthi Narasimha Rao -, Konda Saiteja -, Kamini Vasu -, Raj Kumar P -, Dr.K.S.Ramanujam -
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409395037