Abstract
The world has noticed an alarming surge in ransomware cyberattacks these last years, causing an important financial losses to various organizations. Ransomware attacks are types of malware that usually lock the users’ devices or encrypt their data files and request them to pay money (ransom) to unlock the devices or to recover the encrypted files. Several researches proposed techniques to detect this kind of malwares in their early stages of propagation. However, most of these detection methods followed a signature-based technique, which have difficulties to detect zero-day and unknown ransomware. New techniques that can dynamically identify and stop this type of ransomware are thus desperately needed. In this direction, machine learning and deep learning techniques are recently applied in ransomware detection, spam detection, image recognition, … etc. In this paper, we provide a survey about the ransomware detection studies using machine learning and deep learning techniques, conducted from 2017 to 2022. This paper also provides an in-depth list of possible directions for future study.
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Moujoud, L., Ayache, M., Belmekki, A. (2023). A State-of-the-Art Survey on Ransomware Detection using Machine Learning and Deep Learning. In: Idrissi, A. (eds) Modern Artificial Intelligence and Data Science. Studies in Computational Intelligence, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-031-33309-5_15
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