## 2 Competing Surveys
It was suggested in this work, a blend of machine and deep learning can help to find zero-day ransomware attacks. Each research paper was evaluated in context of limitation and improvement. The study also discussed the population drift concept in ransomware. In [34], a survey was presented that depicted the complete picture on the ransomware and ransomware defense research by considering the diversity of attacked platforms. This survey considered studies until 2020. The focus of this study was to overview the ransomware evolution, attacks, analysis, detection, and defensive mechanisms while considering the different platforms. A survey about analysis and detection of android ransomware attack was presented in [35]. The study primarily focused on ransomware attacks on android operating system. The survey encompassed the studies from 2015 to 2020 that were related to ransomware analysis and detection methods for the android platform. A brief review about different attacks and the use of machine and deep learning techniques to prevent the cybercrimes was discussed in [36]. Phishing, social network spam, malware, ransomware, network intrusion detection studies utilized the machine and deep learning technologies were discussed. The survey presented in [37] discussed the use of deep learning technology in cybersecurity domain. A brief review about the deep learning solutions was described. The survey was outlined by discussing the studies of malware and android malware detection, traffic and binary analysis, spam and phishing detection, and intrusion detection. A study about ransomware evolution, prevention, and mitigation in the domain of IoT was presented in [38]. Ransomware attacks on the IoT devices, its propagation, prevention and mitigation approaches, and challenges were discussed comprehensively. A systematic literature review for windows operating system was presented in [39]. This study discussed different types of ransomwares along with type of encryption used by each ransomware, infection methods, attack methodologies, impacts, vulnerabilities, available mitigation, and prevention from the attacks. A brief survey about ransomware anti-analysis and evasion techniques was presented in [40]. Each of the anti-analysis or evasion techniques were explored precisely. Ransomware attacked platforms, types of ransomwares along with effects of the attacks were also briefly explained. A study presented in [41] summarized the study related to android systems. The detection and protection from ransomware attacks and comparison of those schemes was also presented. A comparison for the analysis results were discussed briefly. The study discussed the different type of analysis carried out in android ransomware studies. The available protection technologies used to prevent android ransomware attacks were described along with comparison of these schemes. A system literature review was presented in [42] for windows and android systems, which summarized the ransomware attacks and protection surveys. The study presented the ransomware attacks taxonomy. Paper also discussed the different ransomware detection techniques, their tackled problems, solutions presented to the problems and limitations of those solutions. The study in [43] presented a survey about deep learning methods in the field of cybersecurity. The study highlighted the security issues faced by the deep learning models. The roles of deep learning and its architectures were briefly discussed. Deep learning studies which considered protection techniques were explored. A comprehensive survey [44] of ransomware detection studies including analysis and synthesis was conducted. The survey highlighted the role of machine learning algorithms for the defensive solutions against ransomware attack. This study also presented brief dataset of ransomware detection studies. Some aspects of deep learning and bigdata were also included in the study. A survey [45] of ransomware detection techniques using machine learning, deep learning, and artificial intelligence was conducted.