{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:17:49Z","timestamp":1753885069228,"version":"3.41.2"},"reference-count":41,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:p> The innovation of technologies has become ubiquitous and imperative in day-to-day lives. Malware is the major threat to the network, and Ransomware is a special and harmful type of malware. Ransomware led to huge data losses and induced huge economic costs. Moreover, Ransomware detection is a crucial task to minimize analyst\u2019s workloads. This paper devises a novel deep learning method for detecting Ransomware using the blockchain network. Here, the sequence-based statistical feature extraction is performed, wherein the features are extracted using 2-gram and 3-gram opcodes. Also, the term frequency-inverse document frequency (TF-IDF) is discovered for each feature. Then the Box-Cox transformation is applied to transformation to the data for improved analysis. Also, the feature fusion is progressed using a fractional concept. Finally, the classification of Ransomware is done using Deep stacked Auto-encoder (Deep SAE), wherein the proposed Water wave-based Moth Flame optimization (WMFO) is adapted for generating the optimal weights. The WMFO is designed by integrating Water wave optimization (WWO) and Moth Flame optimization (MFO). The proposed WMFO-Deep SAE outperformed other methods with maximal accuracy of 96.925%, sensitivity of 96.900%, and specificity of 97.920%. <\/jats:p>","DOI":"10.1142\/s0219691321500223","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T17:09:39Z","timestamp":1618333779000},"source":"Crossref","is-referenced-by-count":3,"title":["Optimized deep stacked autoencoder for ransomware detection using blockchain network"],"prefix":"10.1142","volume":"19","author":[{"given":"G.","family":"Nalinipriya","sequence":"first","affiliation":[{"name":"Department of Information Technology, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu 602105, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Balajee","family":"Maram","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, GMR Institute of Technology, GMR Nagar, Rajam, Andhra Pradesh 532127, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ch.","family":"Vidyadhari","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Kukatpally, Hyderabad, Telangana 500090, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Cristin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, GMR Institute of Technology, GMR Nagar, Rajam, Andhra Pradesh 532127, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"key":"S0219691321500223BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2019.03.012"},{"key":"S0219691321500223BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-017-0379-8"},{"key":"S0219691321500223BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.06.005"},{"key":"S0219691321500223BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.02.249"},{"key":"S0219691321500223BIB007","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/396529"},{"issue":"3","key":"S0219691321500223BIB008","first-page":"40","volume":"2","author":"Chandanapalli S. 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