Understanding Ransomware Attacks: Trends and Prevention Strategies

Abstract

Ransomware attacks pose significant security risks to both personal and corporate data, leading to profound privacy breaches and financial losses. Detecting ransomware accurately and promptly is crucial to mitigating its impact. This paper explores current trends and future prospects in automated ransomware detection, covering a background on ransomware, its historical evolution, and various detection, prevention, mitigation, and recovery approaches. Notably, it presents a comprehensive timeline of ransomware attacks from 1989 to 2021, offering insights into the state-of-the-art techniques, particularly those published between 2015 and 2022. By highlighting recent advancements in machine learning, deep learning, and neural network technologies, this paper identifies research gaps and challenges in combating ransomware, providing valuable direction for future studies.