  Ransomware can produce direct and controllable economic loss, which makes it
one of the most prominent threats in cyber security. As per the latest
statistics, more than half of malwares reported in Q1 of 2017 are ransomware
and there is a potent threat of a novice cybercriminals accessing
rasomware-as-a-service. The concept of public-key based data kidnapping and
subsequent extortion was introduced in 1996. Since then, variants of ransomware
emerged with different cryptosystems and larger key sizes though, the
underlying techniques remained same. Though there are works in literature which
proposes a generic framework to detect the crypto ransomwares, we present a two
step unsupervised detection tool which when suspects a process activity to be
malicious, issues an alarm for further analysis to be carried in the second
step and detects it with minimal traces. The two step detection framework-
RAPPER uses Artificial Neural Network and Fast Fourier Transformation to
develop a highly accurate, fast and reliable solution to ransomware detection
using minimal trace points.
