These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. The increase in the use of artificial intelligence also coincides with this boom in ransomware. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms.
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