SOFT COMPUTING IN PREVENTING RANSOMWARE RELYING ON LARGER-SCALE DATA AND ANALYSIS

Abstract

Ransomware attacks continue to pose a significant threat to organizations and individuals worldwide. The attackers’ ability to constantly evolve and adapt their tactics challenges traditional cybersecurity approaches to keep pace. Ransomware attacks targeting the healthcare industry accounted for 45% of all reported cyberattacks. The nature and scale of attacks and the increasing healthcare technology adoption will continue to pose ransomware attack risks. However, by collecting and analyzing large volumes of data and applying soft computing techniques, cybersecurity experts can improve their ability to detect and prevent ransomware attacks. As a result, soft computing offers options for detecting and preventing malware attacks. Using methods from the field of soft computing, such as fuzzy logic, neural networks, and genetic algorithms, makes it possible to conduct a thorough analysis of large data sets. These can yield insightful information that can help recognize and react to ransomware attacks. These techniques can also help to decrypt files that have been encrypted using ransomware.