Trends and Future Directions in Automated Ransomware Detection
DOI:
https://doi.org/10.33736/jcsi.4932.2022Keywords:
machine learning, deep learning, neural network, ransomware attack, ransomware detection, securityAbstract
Ransomware attacks constitute major security threats to personal and corporate data and information. A successful ransomware attack results in significant security and privacy violations with attendant financial losses and reputational damages to owners of computer-based resources. This makes it imperative for accurate, timely and reliable detection of ransomware. Several techniques have been proposed for ransomware detection and each technique has its strengths and limitations. The aim of this paper is to discuss the current trends and future directions in automated ransomware detection. The paper provides a background discussion on ransomware as well as historical background and chronology of ransomware attacks. It also provides a detailed and critical review of recent approaches to ransomware detection, prevention, mitigation and recovery. A major strength of the paper is the presentation of the chronology of ransomware attacks from its inception in 1989 to the latest attacks occurring in 2021. Another strength of the study is that a large proportion of the studies reviewed were published between 2015 and 2022. This provides readers with an up-to-date knowledge of the state-of-the-art in ransomware detection. It also provides insights into advances in strategies for preventing, mitigating and recovering from ransomware attacks. Overall, this paper presents researchers with open issues and possible research problems in ransomware detection, prevention, mitigation and recovery.
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