Abstract
Ransomware is a malware which may publish the users data or may block genuine access to it unless a ransom is paid by the user. This kind of malware belongs to cryptovirology. It has become increasingly popular as a cyber threat and is highly destructive, causing an immense loss for unprepared users and businesses. In this work, we use a data set of about 50K samples, out of which, about 23K are ransomware, and 27K are benign. The malware samples are downloaded from publicly available repositories such as Virusshare, and benign files are crawled from online software hosting websites. We design and deploy a static analysis tool using machine learning that scans and gives general information while also detecting the nature of a portable executable file given as input. Our model offers an accuracy of 99.68%. We also provide a command-line based application using Python that shows general file information and characteristics and predicts the malicious nature of the given portable executable.
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Gaur, K., Kumar, N., Handa, A., Shukla, S.K. (2021). Static Ransomware Analysis Using Machine Learning and Deep Learning Models. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_30
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DOI: https://doi.org/10.1007/978-981-33-6835-4_30
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