Crypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for a ransom to recover the hijacked documents. It is a cyber threat that targets both companies and residential users, and has spread in recent years because of its lucrative results. Several articles have presented classifications of ransomware families and their typical behaviour. These insights have stimulated the creation of detection techniques for antivirus and firewall software. However, because the ransomware scene evolves quickly and aggressively, these studies quickly become outdated. In this study, we surveyed the detection techniques that the research community has developed in recent years. We compared the different approaches and classified the algorithms based on the input data they obtain from ransomware actions, and the decision procedures they use to reach a classification decision between benign or malign applications. This is a detailed survey that focuses on detection algorithms, compared to most previous studies that offer a survey of ransomware families or isolated proposals of detection algorithms. We also compared the results of these proposals.

Crypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for ransom to recover the hijacked documents. Several articles have presented detection techniques for this type of malware; these techniques are applied before the ransomware encrypts files or during its action in an infected host. The evaluation of these proposals has always been accomplished using sets of ransomware samples that are prepared locally for the research article, without making the data available. Different studies use different sets of samples and different evaluation metrics, resulting in insufficient comparability. In this paper, we describe a public data repository containing the file access operations of more than 70 ransomware samples during the encryption of a large network shared directory. These data have already been used successfully in the evaluation of a network-based ransomware detection algorithm. Now, we are making these data available to the community and describing their details, how they were captured, and how they can be used in the evaluation and comparison of the results of most ransomware detection techniques.