Crow Search with Adaptive Awareness Probability-Based Deep Belief Network for Detecting Ransomware

Abstract

“Crypto ransomware is defined as malware that blocks the user file’s access by encrypting them and demands them with a ransom for obtaining the decryption key”. This causes some major threats in many of the companies. Therefore, the detection of ransomware is needful for reducing the workloads of the analysts and also for finding the variations in unknown samples. The adopted scheme encompasses 3 phases: (i) feature extraction, (ii) feature selection and (iii) classification. Initially, the sequential frequent patterns are extracted using the apriori framework. However, the major challenge in this extracted sequential pattern is the curse of dimensionality. To overcome this, the selection of optimal features is very important, which is done as the second stage. In this, the optimization concept is evolved for the optimal selection of these extracted sequential patterns. Furthermore, the optimal patterns are given for classification, where DBN is deployed. Particularly, for the selection of the optimal sequential pattern, this work proposes a new crow search with adaptive awareness probability (CS-AAP) model. In the end, analysis is performed to authorize the supremacy of the developed scheme.