{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:38:00Z","timestamp":1774993080152,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble\u2014autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models\u2014autoencoder, GRU, and MLP\u2014that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them.<\/jats:p>","DOI":"10.3390\/s23063253","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:30:31Z","timestamp":1679283031000},"page":"3253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Transfer Learning for Image-Based Malware Detection for IoT"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9812-3055","authenticated-orcid":false,"given":"Pratyush","family":"Panda","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2866-0281","authenticated-orcid":false,"given":"Om Kumar","family":"C U","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0830-1110","authenticated-orcid":false,"given":"Suguna","family":"Marappan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3796-3623","authenticated-orcid":false,"given":"Suresh","family":"Ma","sequence":"additional","affiliation":[{"name":"Amrita School of Business, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1837-6797","authenticated-orcid":false,"given":"Manimurugan","family":"S","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia"}]},{"given":"Deeksha","family":"Veesani Nandi","sequence":"additional","affiliation":[{"name":"Technical Lead, Virtusa Consulting Services, Chennai 603103, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","unstructured":"Wikipedia (2022, December 06). Computer Security. Available online: https:\/\/en.wikipedia.org\/wiki\/Computer_security."},{"key":"ref_2","unstructured":"SpringerLink (2023, February 23). Fuzzy Mathematics: An Introduction for Engineers and Scientists. Available online: https:\/\/link.springer.com\/book\/10.1007\/978-3-7908-1808-6."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Debnath, P., and Mohiuddine, S.A. (2021). Soft Computing Techniques in Engineering, Health, Mathematical and Social Sciences, CRC Press. [1st ed.].","DOI":"10.1201\/9781003161707"},{"key":"ref_4","unstructured":"Kumar, C.O., Tejaswi, K., and Bhargavi, P. (2013, January 21\u201322). A distributed cloud-prevents attacks and preserves user privacy. Proceedings of the 2013 15th International Conference on Advanced Computing Technologies (ICACT), Rajampet, India."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8312","DOI":"10.1007\/s11227-019-03005-2","article-title":"Detecting and confronting flash attacks from IoT botnets","volume":"75","year":"2019","journal-title":"J. Supercomput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Singh, N.K., Kumar, C.O., and Sridhar, R. (2017, January 6\u20137). Flash crowd prediction in Twitter. Proceedings of the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS.2017.8014676"},{"key":"ref_7","unstructured":"CU, O.K., and Sathia Bhama, P.R. (2021). Efficient ensemble to combat flash attacks. Comput. Intell., online version of record."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e6918","DOI":"10.1002\/cpe.6918","article-title":"Effective intrusion detection system for IoT using optimized capsule auto encoder model","volume":"34","author":"Durairaj","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/978-981-16-1338-8_32","article-title":"Proficient Detection of Flash Attacks Using a Predictive Strategy","volume":"Volume 789","author":"Shetty","year":"2022","journal-title":"Emerging Research in Computing, Information, Communication and Applications"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Om Kumar, C.U., Marappan, S., Murugeshan, B., and Beaulah, V. (2022). Intrusion Detection Model for IoT Using Recurrent Kernel Convolutional Neural Network. Wirel. Pers. Commun., 1\u201330.","DOI":"10.1007\/s11277-022-10155-9"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJCWT.311422","article-title":"Malevolent Information Crawling Mechanism for Forming Structured Illegal Organisations in Hidden Networks","volume":"12","author":"Rawat","year":"2022","journal-title":"Int. J. Cyber Warf. Terror."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/978-981-19-5482-5_6","article-title":"Efficacious intrusion detection on cloud using improved BES and HYBRID SKINET-EKNN","volume":"Volume 928","author":"Shetty","year":"2023","journal-title":"Emerging Research in Computing, Information, Communication and Applications"},{"key":"ref_13","unstructured":"CU, O.K., Pranavi, D., Laxmi, B.A., and Devasena, R. (2022). Using Computational Intelligence for the Dark Web and Illicit Behavior Detection, IGI Global."},{"key":"ref_14","unstructured":"Wikipedia (2022, December 06). Malware. Available online: https:\/\/en.wikipedia.org\/wiki\/Malware."},{"key":"ref_15","unstructured":"Financesonline.com (2022, December 11). Number of Smartphone and Mobile Phone Users Worldwide in 2022\/2023: Demographics, Statistics, Predictions. Available online: https:\/\/financesonline.com\/number-of-smartphone-users-worldwide\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3530814","article-title":"A systematic survey on android api usage for data-driven analytics with smartphones","volume":"55","author":"Lee","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_17","first-page":"212","article-title":"Ransomware steals your phone","volume":"Volume 9688","author":"Abert","year":"2016","journal-title":"Formal methods rescue it. In Proceedings of the International Conference on Formal Techniques for Distributed Objects, Components, and Systems"},{"key":"ref_18","unstructured":"Marulli, F., and Visaggio, C.A. (2019, January 22\u201324). Adversarial deep learning for energy management in buildings. Proceedings of the SummerSim \u201819: 2019 Summer Simulation Conference, Berlin, Germany."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSEC.2020.3012059","article-title":"Privacy regulations, smart roads, blockchain, and liability insurance: Putting technologies to work","volume":"19","author":"Campanile","year":"2020","journal-title":"IEEE Secur. Priv."},{"key":"ref_20","unstructured":"(2022, December 10). Malware Statistics in 2023: Frequency, Impact, Cost & More. Available online: https:\/\/www.comparitech.com\/antivirus\/malware-statistics-facts\/."},{"key":"ref_21","unstructured":"(2022, December 11). April 12, 2021\u2014Check Point Software. Available online: https:\/\/blog.checkpoint.com\/2021\/04\/12\/."},{"key":"ref_22","unstructured":"(2022, December 07). Google Safe Browsing\u2014Google Transparency Report. Available online: https:\/\/transparencyreport.google.com\/safe-browsing\/overview?hl=en_GB&unsafe=dataset:1;series:malwareDetected,phishingDetected;start:1148194800000;end:1612080000000&lu=unsafe."},{"key":"ref_23","unstructured":"Statista (2022, December 07). Our Research and Content Philosophy. Available online: https:\/\/www.statista.com\/aboutus\/our-research-commitment."},{"key":"ref_24","unstructured":"(2022, December 07). Global Ransomware Damage Costs Predicted to Exceed $265 Billion By 2031. Available online: https:\/\/cybersecurityventures.com\/global-ransomware-damage-costs-predicted-to-reach-250-billion-usd-by-2031\/#:~:text=2022%20Ransomware%20Market%20Report%20is%20sponsored%20by%20KnowBe4&text=The%20damages%20for%202018%20were,than%20it%20was%20in%202015."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s11416-018-0324-z","article-title":"Analysis of ResNet and GoogleNet models for malware detection","volume":"15","author":"Khan","year":"2019","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.diin.2018.09.006","article-title":"A malware classification method based on memory dump grayscale image","volume":"27","author":"Dai","year":"2018","journal-title":"Digit. Investig."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1145\/2508148.2485970","article-title":"On the feasibility of online malware detection with performance counters","volume":"41","author":"Demme","year":"2013","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"ref_28","unstructured":"Tang, A., Sethumadhavan, S., and Stolfo, S.J. (2014). International Workshop on Recent Advances in Intrusion Detection, Springer."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_30","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Marastoni, N., Giacobazzi, R., and Dalla Preda, M. (2018, January 3). A deep learning approach to program similarity. Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, Montpellier, France.","DOI":"10.1145\/3243127.3243131"},{"key":"ref_32","unstructured":"Wikipedia (2022, December 07). Transfer Learning. Available online: https:\/\/en.wikipedia.org\/wiki\/Transfer_learning."},{"key":"ref_33","unstructured":"Transfer Learning (2022, December 11). Pretrained Models in Deep Learning. Available online: https:\/\/www.analyticsvidhya.com\/blog\/2017\/06\/transfer-learning-the-art-of-fine-tuning-a-pre-trained-model\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s11416-021-00381-3","article-title":"Data augmentation and transfer learning to classify malware images in a deep learning context","volume":"17","author":"Marastoni","year":"2021","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.procs.2022.09.047","article-title":"On the Resilience of Shallow Machine Learning Classification in Image-based Malware Detection","volume":"207","author":"Casolare","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","first-page":"1352","article-title":"Image-based malware classification using convolutional neural network","volume":"Volume 474","author":"Park","year":"2017","journal-title":"Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017"},{"key":"ref_37","first-page":"75","article-title":"Malware classification using image representation","volume":"Volume 11527","author":"Dolev","year":"2019","journal-title":"International Symposium on Cyber Security Cryptography and Machine Learning. CSCML 2019"},{"key":"ref_38","first-page":"377","article-title":"A hybrid deep learning image-based analysis for effective malware detection","volume":"47","author":"Venkatraman","year":"2019","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101748","DOI":"10.1016\/j.cose.2020.101748","article-title":"Image-Based malware classification using ensemble of CNN architectures (IMCEC)","volume":"92","author":"Vasan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/978-981-15-2414-1_33","article-title":"A deep learning approach to image-based malware analysis","volume":"Volume 1119","author":"Das","year":"2020","journal-title":"Progress in Computing, Analytics and Networking"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107138","DOI":"10.1016\/j.comnet.2020.107138","article-title":"IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture","volume":"171","author":"Vasan","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1007\/s00521-020-05195-w","article-title":"VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques","volume":"33","author":"Bakour","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.future.2021.06.029","article-title":"MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of Things","volume":"125","author":"Kumar","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Anandhi, V., Vinod, P., and Menon, V.G. (2021). Malware visualization and detection using DenseNets. Pers. Ubiquitous Comput., 1\u201317.","DOI":"10.1007\/s00779-021-01581-w"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pant, D., and Bista, R. (2021, January 26\u201328). Image-based Malware Classification using Deep Convolutional Neural Network and Transfer Learning. Proceedings of the 2021 3rd International Conference on Advanced Information Science and System (AISS 2021), Sanya, China.","DOI":"10.1145\/3503047.3503081"},{"key":"ref_46","first-page":"103063","article-title":"DTMIC: Deep transfer learning for malware image classification","volume":"64","author":"Kumar","year":"2022","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kalash, M., Rochan, M., Mohammed, N., Bruce, N.D., Wang, Y., and Iqbal, F. (2018, January 26\u201328). Malware classification with deep convolutional neural networks. Proceedings of the 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France.","DOI":"10.1109\/NTMS.2018.8328749"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1007\/s42452-020-3132-2","article-title":"Android malware detection based on image-based features and machine learning techniques","volume":"2","author":"Bakour","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jin, X., Xing, X., Elahi, H., Wang, G., and Jiang, H. (2020, January 10\u201313). A malware detection approach using malware images and autoencoders. Proceedings of the 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Delhi, India.","DOI":"10.1109\/MASS50613.2020.00009"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"11499","DOI":"10.1007\/s00521-021-05816-y","article-title":"DeepVisDroid: Android malware detection by hybridizing image-based features with deep learning techniques","volume":"33","author":"Bakour","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lo, W.W., Yang, X., and Wang, Y. (2019, January 24\u201326). An xception convolutional neural network for malware classification with transfer learning. Proceedings of the 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Canary Islands, Spain.","DOI":"10.1109\/NTMS.2019.8763852"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"30997","DOI":"10.1007\/s11042-022-12615-7","article-title":"S-DCNN: Stacked deep convolutional neural networks for malware classification","volume":"81","author":"Parihar","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_53","unstructured":"Kaggle (2022, December 10). Malimg_Dataset9010. Available online: https:\/\/www.kaggle.com\/datasets\/keerthicheepurupalli\/malimg-dataset9010."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.future.2021.06.032","article-title":"Visualization and deep-learning-based malware variant detection using OpCode-level features","volume":"125","author":"Darem","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"206303","DOI":"10.1109\/ACCESS.2020.3036491","article-title":"Intelligent vision-based malware detection and classification using deep random forest paradigm","volume":"8","author":"Roseline","year":"2020","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ding, Y., Zhang, X., Hu, J., and Xu, W. (2020). Android malware detection method based on bytecode image. J. Ambient. Intell. Humaniz. Comput., 1\u201310.","DOI":"10.1007\/s12652-020-02196-4"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.icte.2020.04.005","article-title":"A survey of IoT malware and detection methods based on static features","volume":"6","author":"Ngo","year":"2020","journal-title":"ICT Express"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s11265-020-01588-1","article-title":"A method for windows malware detection based on deep learning","volume":"93","author":"Huang","year":"2021","journal-title":"J. Signal Process. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"102154","DOI":"10.1016\/j.adhoc.2020.102154","article-title":"Malware detection in industrial internet of things based on hybrid image visualization and deep learning model","volume":"105","author":"Naeem","year":"2020","journal-title":"Ad Hoc Netw."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"He, K., and Kim, D.S. (2019, January 5\u20138). Malware detection with malware images using deep learning techniques. Proceedings of the 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications\/13th IEEE International Conference On Big Data Science And Engineering (TrustCom\/BigDataSE), Rotorua, New Zealand.","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00022"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Su, J., Vasconcellos, D.V., Prasad, S., Sgandurra, D., Feng, Y., and Sakurai, K. (2018, January 23\u201327). Lightweight classification of IoT malware based on image recognition. Proceedings of the 2018 IEEE 42Nd annual computer software and applications conference (COMPSAC), Tokyo, Japan.","DOI":"10.1109\/COMPSAC.2018.10315"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"15498","DOI":"10.1038\/s41598-022-18936-9","article-title":"IoT malware detection architecture using a novel channel boosted and squeezed CNN","volume":"12","author":"Asam","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Makandar, A., and Patrot, A. (2017, January 24\u201326). Malware class recognition using image processing techniques. Proceedings of the 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), Pune, India.","DOI":"10.1109\/ICDMAI.2017.8073489"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bhodia, N., Prajapati, P., Di Troia, F., and Stamp, M. (2019, January 23\u201325). Transfer learning for image-based malware classification. Proceedings of the 3rd International Workshop on Formal Methods for Security Engineering (ForSE 2019), in Conjunction with the 5th International Conference on Information Systems Security and Privacy (ICISSP 2019), Prague, Czech Republic.","DOI":"10.5220\/0007701407190726"},{"key":"ref_65","unstructured":"Kaggle (2022, December 10). MaleVis Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/nimit5\/malevis-dataset."},{"key":"ref_66","unstructured":"(2022, December 10). MaleVis Dataset Home Page. Available online: https:\/\/web.cs.hacettepe.edu.tr\/~selman\/malevis\/."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Nataraj, L., Karthikeyan, S., Jacob, G., and Manjunath, B. (2011, January 20). Malware images: Visualization and automatic classification. Proceedings of the 8th International Symposium on Visualization for Cyber Security, Pittsburgh, PA, USA.","DOI":"10.1145\/2016904.2016908"},{"key":"ref_68","unstructured":"(2022, December 17). Model Plotting Utilities. Available online: https:\/\/keras.io\/api\/utils\/model_plotting_utils\/."},{"key":"ref_69","unstructured":"(2022, December 17). Download|Graphviz. Available online: https:\/\/graphviz.gitlab.io\/download\/."},{"key":"ref_70","unstructured":"(2022, December 18). Applied Deep Learning\u2014Part 3: Autoencoders|by Arden Dertat|Towards Data Science. Available online: https:\/\/towardsdatascience.com\/applied-deep-learning-part-3-autoencoders-1c083af4d798."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_72","unstructured":"Understanding GRU Networks (2022, December 18). In This Article, I Will Try to Give a\u2026 |by Simeon Kostadinov|Towards Data Science. Available online: https:\/\/towardsdatascience.com\/understanding-gru-networks-2ef37df6c9be."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-981-19-1122-4_1","article-title":"A Novel Approach for Detecting Online Malware Detection LSTMRNN and GRU Based Recurrent Neural Network in Cloud Environment","volume":"Volume 434","author":"Rathore","year":"2022","journal-title":"Rising Threats in Expert Applications and Solutions"},{"key":"ref_74","unstructured":"(2022, December 20). sklearn.neural_network.MLPClassifier\u2014scikit-learn 1.2.0 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neural_network.MLPClassifier.html."},{"key":"ref_75","unstructured":"(2022, December 20). sklearn.metrics.classification_report\u2014scikit-learn 1.2.0 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.classification_report.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:58:56Z","timestamp":1760122736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,20]]},"references-count":75,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063253"],"URL":"https:\/\/doi.org\/10.3390\/s23063253","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,20]]}}}