![]() ![]() ![]() This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. The article emphasizes the role of the developer in secure application design. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. This article aims to provide a comprehensive study of the IoT and Android systems. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. These are affordable, easy-to-use, and open-source technology. The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. This research also contributes to constructing an up-to-date, unique dataset that covers the majority of existing Android ransomware families and recent clean apps that could be used as a labeled reference for research community. 2959 ransomware samples were collected, tested and reduced by almost 83% due to samples duplication. Moreover, this research designed a proactive mechanism based on a high quality unique ransomware dataset without duplicated samples. API-RDS achieved 97% accuracy while reducing the complexity of the classification model by 26% due to features reduction. The experimental results show that API-RDS outperformed other recent related approaches. Significant API packages with corresponding methods were identified. API packages’ calls of both benign and ransomware apps were thoroughly analyzed and compared. API-RDS focuses on examining API packages’ calls as leading indicator of ransomware activity to discriminate ransomware with high accuracy before it harms the user’s device. An application programming interface (API)-based ransomware detection system (API-RDS) was proposed to provide a static analysis paradigm for detecting Android ransomware apps. A deep comparative analysis was conducted which shed the key differences among the existing solutions. In this paper, the state-of-the-art of Android ransomware detection approaches were investigated. Additionally, there are plenty of open-source malware datasets however, the research community is still lacking ransomware datasets. Moreover, the literature counts only a few studies that have proposed static and/or dynamic approaches to detect Android ransomware in particular. The available technologies are not enough as new ransomwares employ a combination of techniques to evade anti-virus detection. Ransomware in general encrypts or locks the files on the victim’s device and requests a payment in order to recover them. Android ransomware is one of the most threatening attacks nowadays. ![]()
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