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(NAECON)","first-page":"70","article-title":"Classification of malware programs using autoencoders based deep learning architecture and its application to the microsoft Malware classification challenge (BIG 2015) dataset","author":"Kebede","year":"Jun. 27\u201330, 2017"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MSP.2007.45","article-title":"Toward automated dynamic malware analysis using CW sandbox","volume":"5","author":"Willems","year":"2007","journal-title":"IEEE Security & Privacy"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13673-018-0125-x","article-title":"A state-of-the-art survey of malware detection approaches using data mining techniques","volume":"8","author":"Souri","year":"2018","journal-title":"Hum. Centric Comput. Inf. Sci."},{"key":"ref11","series-title":"Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP)","first-page":"3422","article-title":"Large-scale malware classification using random projections and neural networks","author":"Dahl","year":"May 26\u201331, 2013"},{"key":"ref12","first-page":"56","article-title":"Malware analysis and classification: A survey","volume":"5","author":"Gandotra","year":"2014","journal-title":"J. Inf. Secur."},{"key":"ref13","series-title":"12th Int. Symp.","first-page":"121","article-title":"PE-Miner: Mining structural information to detect malicious executables in realtime","author":"Shafiq","year":"Sep. 2009"},{"key":"ref14","series-title":"IEEE Int. Conf. on Commun. (ICC)","first-page":"1","article-title":"Malware classification method based on word vector of bytes and multilayer perception","author":"Yanchen","year":"Jun. 2020"},{"key":"ref15","article-title":"Polymorphic malware detection using sequence classification methods and ensembles","volume":"2017","author":"Drew","year":"2017","journal-title":"EURASIP J. Inf. 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Comput. Syst."},{"key":"ref20","doi-asserted-by":"crossref","first-page":"6446","DOI":"10.3390\/app11146446","article-title":"Visualized malware multi-classification framework using fine-tuned CNN-based transfer learning models","volume":"11","author":"El-Shafai","year":"Jul. 2021","journal-title":"Appl. Sci."},{"key":"ref21","first-page":"100546","article-title":"A survey of malware detection using deep learning","volume":"16","author":"Bensaoud","year":"Jun. 2024","journal-title":"Machine Learn. Appl."},{"key":"ref22","series-title":"Proc. Int. Conf. Data Mining Steering Committee World Congr. Comput. Sci. (DMIN)","first-page":"61","article-title":"DL4MD: A deep learning framework for intelligent malware detection","author":"Hardy","year":"Jul. 2016"},{"key":"ref23","first-page":"176","article-title":"Tools & techniques for malware analysis and classification","volume":"7","author":"Gandotra","year":"2016","journal-title":"Int. J. Next-Generation Comput."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s00521-017-3077-6","article-title":"Malware detection based on deep learning algorithm","volume":"31","author":"Yuxin","year":"Feb. 2020","journal-title":"Neural Comput. Appl."},{"key":"ref25","doi-asserted-by":"crossref","first-page":"100624","DOI":"10.1016\/j.measen.2022.100624","article-title":"Enhancing the security in cyber-world by detecting the botnets using ensemble classification-based machine learning","volume":"25","author":"Srinivasan","year":"Feb. 2023","journal-title":"Meas. Sens."},{"key":"ref26","doi-asserted-by":"crossref","first-page":"800","DOI":"10.3390\/jcp2040041","article-title":"A survey of the recent trends in deep learning based malware detection","volume":"2","author":"Tayyab","year":"Sep. 2022","journal-title":"J. Cybersecurity Privacy"},{"key":"ref27","series-title":"12th ACM Workshop on Artif. 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