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This concept aims to establish novel malware detection in WSN that consists of several phases: \u201c(i) Preprocessing, (ii) feature extraction, as well as (iii) detection\u201d. At first, the input data is subjected for preprocessing phase. Then, the feature extraction takes place, in which principal component analysis (PCA), improved linear discriminant analysis (LDA), and autoencoder-based characteristics are retrieved. Moreover, the retrieved characteristics are subjected to the detection phase. The detection is performed employing combined shallow learning and DL. Further, the shallow learning includes decision tree (DT), logistic regression (LR), and Naive Bayes (NB); the deep learning (DL) includes deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Here, the DT output is given to the DNN, LR output is subjected to CNN, and the NB output is given to the RNN, respectively. Eventually, the DNN, CNN, and RNN outputs are averaged to generate a successful outcome. The combination can be thought of as an Ensemble classifier. The weight of the RNN is optimally tuned through the Self Improved Shark Smell Optimization with Opposition Learning (SISSOOL) model to improve detection precision and accuracy. Lastly, the outcomes of the suggested approach\u00a0are computed in terms of\u00a0different measures. <\/jats:p>","DOI":"10.1142\/s0219467825500342","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T09:23:16Z","timestamp":1694078596000},"source":"Crossref","is-referenced-by-count":1,"title":["Combined Shallow and Deep Learning Models for Malware Detection in Wsn"],"prefix":"10.1142","volume":"25","author":[{"given":"Madhavarapu","family":"Chandan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu 608002, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. 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