  Crypto-ransomware is characterized by its irreversible effect even after the
detection and removal. As such, the early detection is crucial to protect user
data and files of being held to ransom. Several solutions have proposed
utilizing the data extracted during the initial phases of the attacks before
the encryption takes place. However, the lack of enough data at the early
phases of the attack along with high dimensional features space renders the
model prone to overfitting which decreases its detection accuracy. To this end,
this paper proposed a novel redundancy coefficient gradual up-weighting
approach that was incorporated to the calculation of redundancy term of mutual
information to improve the feature selection process and enhance the accuracy
of the detection model. Several machine learning classifiers were used to
evaluate the detection performance of the proposed techniques. The experimental
results show that the accuracy of proposed techniques achieved higher detection
accuracy. Those results demonstrate the efficacy of the proposed techniques for
the early detection tasks.
