Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN

Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN Ganapathi Nalinipriya Department of Information Technology Saveetha Engineering College Chennai Thandalam Tamil Nadu India https://orcid.org/0000-0001-9330-9231 Maram Balajee Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Himachal Pradesh India Chittibabu Priya Department of Information Technology Vels Institute of Science, Technology and Advanced Studies Chennai Pallavaram Tamil Nadu India Cristin Rajan Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Himachal Pradesh India Department of Computer Science and Engineering GMR Institute of Technology Razam Andhra Pradesh India Abstract

The emergence of networking systems and quick deployment of applications cause huge increase in cybercrimes which involves various applications like phishing, hacking, and malware propagation. However, the Ransomware techniques utilize certain device which may lead to undesirable properties which might shrink the paying‐victim pool. This paper devises a new method, namely Water Moth Flame optimization (WMFO) and deep recurrent neural network (Deep RNN) for determining Ransomware. Here, Deep RNN training is done with WMFO, and is developed by combining Moth Flame optimization (MFO) and Water wave optimization (WWO). Moreover, features are mined with opcodes and by finding term frequency‐inverse document frequency (TF‐IDF) amongst individual features. Moreover, Probabilistic Principal Component Analysis (PPCA) is adapted to choose significant features. These features are adapted in Deep RNN for classification, wherein the proposed WMFO is employed to produce optimum weights. The WMFO offered enhanced performance with elevated accuracy of 95.025%, sensitivity of 95%, and specificity of 96%.
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