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Surv."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","article-title":"Anomaly-based network intrusion detection: Techniques, systems and challenges","volume":"28","author":"Garcia-Teodoro","year":"2009","journal-title":"Comput. Secur."},{"key":"ref12","series-title":"Proc. 23rd ACM SIGKDD Inter. Conf. Knowled. Discover. Data Mining","first-page":"1723","article-title":"Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity","author":"Anderson","year":"2017"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.dcan.2022.09.009","article-title":"Network traffic classification: Techniques, datasets, and challenges","volume":"10","author":"Azab","year":"Jun. 2024","journal-title":"Digit. Commun. Netw."},{"key":"ref14","series-title":"Proc. 3rd Int. Conf. Inform. Syst. Secur. 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