{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T16:11:32Z","timestamp":1762272692943,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T00:00:00Z","timestamp":1608595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,22]]},"DOI":"10.1145\/3428363.3428365","type":"proceedings-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T14:40:56Z","timestamp":1608648056000},"page":"45-55","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning Based Malware Detection on Encrypted Traffic: A Comprehensive Performance Study"],"prefix":"10.1145","author":[{"given":"Onur","family":"Barut","sequence":"first","affiliation":[{"name":"University of Massachusetts Lowell, USA"}]},{"given":"Matthew","family":"Grohotolski","sequence":"additional","affiliation":[{"name":"Elizabethtown College, USA"}]},{"given":"Connor","family":"DiLeo","sequence":"additional","affiliation":[{"name":"Elizabethtown College, USA"}]},{"given":"Yan","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Massachusetts Lowell, USA"}]},{"given":"Peilong","family":"Li","sequence":"additional","affiliation":[{"name":"Elizabethtown College, USA"}]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intel Corporation, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2841987"},{"volume-title":"Investigating Two Different Approaches for Encrypted Traffic Classification. In 2008 Sixth Annual Conference on Privacy, Security and Trust. 156\u2013166","year":"2008","author":"Alshammari R.","key":"e_1_3_2_1_2_1"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Blake Anderson Subharthi Paul and David McGrew. 2016. Deciphering Malware\u2019s use of TLS (without Decryption). Journal of Computer Virology and Hacking Techniques (07 2016). https:\/\/doi.org\/10.1007\/s11416-017-0306-6  Blake Anderson Subharthi Paul and David McGrew. 2016. Deciphering Malware\u2019s use of TLS (without Decryption). Journal of Computer Virology and Hacking Techniques (07 2016). https:\/\/doi.org\/10.1007\/s11416-017-0306-6","DOI":"10.1007\/s11416-017-0306-6"},{"volume-title":"Machine Learning and Knowledge Discovery in Databases, Albert Bifet, Michael May, Bianca Zadrozny, Ricard Gavalda","author":"Bartos Karel","key":"e_1_3_2_1_4_1"},{"key":"e_1_3_2_1_5_1","unstructured":"Canadian Institute for Cybersecurity. 2004. ISCX Datasets. https:\/\/www.unb.ca\/cic\/datasets\/index.html [Online; accessed 23-July-2020].  Canadian Institute for Cybersecurity. 2004. ISCX Datasets. https:\/\/www.unb.ca\/cic\/datasets\/index.html [Online; accessed 23-July-2020]."},{"volume-title":"CICIDS2017 General Information.2020","year":"2017","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","unstructured":"Blake\u00a0Anderson David\u00a0McGrew 2017. Joy. https:\/\/github.com\/cisco\/joy  Blake\u00a0Anderson David\u00a0McGrew 2017. Joy. https:\/\/github.com\/cisco\/joy"},{"volume-title":"International Journal of Network Security & Its Applications 2 (04","year":"2010","author":"Farid Dewan","key":"e_1_3_2_1_8_1"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Minghui Gao Li Ma Heng Liu Zhijun Zhang Zhiyan Ning and Jian Xu. 2020. Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis. Sensors 20 (03 2020) 1452. https:\/\/doi.org\/10.3390\/s20051452  Minghui Gao Li Ma Heng Liu Zhijun Zhang Zhiyan Ning and Jian Xu. 2020. Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis. Sensors 20 (03 2020) 1452. https:\/\/doi.org\/10.3390\/s20051452","DOI":"10.3390\/s20051452"},{"key":"e_1_3_2_1_10_1","unstructured":"Google. 2020. Encrypted traffic across Google. https:\/\/transparencyreport.google.com\/https\/overview?hl=en [Online; accessed 20-June-2020].  Google. 2020. Encrypted traffic across Google. https:\/\/transparencyreport.google.com\/https\/overview?hl=en [Online; accessed 20-June-2020]."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Arash Habibi\u00a0Lashkari Gerard Draper\u00a0Gil Mohammad Mamun and Ali Ghorbani. 2017. Characterization of Tor Traffic using Time based Features. 253\u2013262. https:\/\/doi.org\/10.5220\/0006105602530262  Arash Habibi\u00a0Lashkari Gerard Draper\u00a0Gil Mohammad Mamun and Ali Ghorbani. 2017. Characterization of Tor Traffic using Time based Features. 253\u2013262. https:\/\/doi.org\/10.5220\/0006105602530262","DOI":"10.5220\/0006105602530262"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976908"},{"key":"e_1_3_2_1_13_1","first-page":"2","article-title":"AdaBoost-Based Algorithm for Network Intrusion Detection","volume":"38","author":"Hu W.","year":"2008","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"e_1_3_2_1_14_1","unstructured":"Intel Corporation. 2019. Intel\u00ae Data Analytics Acceleration Library (Intel\u00ae DAAL). https:\/\/software.intel.com\/en-us\/intel-daal  Intel Corporation. 2019. Intel\u00ae Data Analytics Acceleration Library (Intel\u00ae DAAL). https:\/\/software.intel.com\/en-us\/intel-daal"},{"key":"e_1_3_2_1_15_1","unstructured":"Intel Corporation. 2019. OpenVINO Toolkit: Develop Multiplatform Computer Vision Solutions. Explore the Intel\u00ae Distribution of OpenVINO\u2122 toolkit. https:\/\/software.intel.com\/en-us\/openvino-toolkit  Intel Corporation. 2019. OpenVINO Toolkit: Develop Multiplatform Computer Vision Solutions. Explore the Intel\u00ae Distribution of OpenVINO\u2122 toolkit. https:\/\/software.intel.com\/en-us\/openvino-toolkit"},{"key":"e_1_3_2_1_16_1","unstructured":"[\n  16\n  ]  KDD Cup 1999 Data.1999. http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html[Online; accessed 14-March-2020].  [16] KDD Cup 1999 Data.1999. http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html[Online; accessed 14-March-2020]."},{"volume-title":"Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. In 2016 International Conference on Platform Technology and Service (PlatCon). 1\u20135.","author":"Kim J.","key":"e_1_3_2_1_17_1"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Y. Liu S. Liu and X. Zhao. 2018. Intrusion Detection Algorithm Based on Convolutional Neural Network. DEStech Transactions on Engineering and Technology Research (03 2018). https:\/\/doi.org\/10.12783\/dtetr\/iceta2017\/19916  Y. Liu S. Liu and X. Zhao. 2018. Intrusion Detection Algorithm Based on Convolutional Neural Network. DEStech Transactions on Engineering and Technology Research (03 2018). https:\/\/doi.org\/10.12783\/dtetr\/iceta2017\/19916","DOI":"10.12783\/dtetr\/iceta2017\/19916"},{"volume-title":"Proceedings of the 4th ACM\/IEEE Symposium on Architectures for Networking and Communications Systems(ANCS \u201908)","author":"Luo Y.","key":"e_1_3_2_1_19_1"},{"volume-title":"ACETA: Accelerating Encrypted Traffic Analytics on Network Edge. In IEEE ICC 2020","year":"2020","author":"Manning D.","key":"e_1_3_2_1_20_1"},{"volume-title":"Deep in the Dark - Deep Learning-Based Malware Traffic Detection Without Expert Knowledge. In 2019 IEEE Security and Privacy Workshops (SPW). 36\u201342","author":"Mar\u00edn G.","key":"e_1_3_2_1_21_1"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIAS.2010.5604193"},{"volume-title":"2017 IEEE Security and Privacy Workshops (SPW). 205\u2013210","author":"Prasse P.","key":"e_1_3_2_1_23_1"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Vinayakumar R Soman Kp and Prabaharan Poornachandran. 2017. Evaluating effectiveness of shallow and deep networks to intrusion detection system. 1282\u20131289. https:\/\/doi.org\/10.1109\/ICACCI.2017.8126018  Vinayakumar R Soman Kp and Prabaharan Poornachandran. 2017. Evaluating effectiveness of shallow and deep networks to intrusion detection system. 1282\u20131289. https:\/\/doi.org\/10.1109\/ICACCI.2017.8126018","DOI":"10.1109\/ICACCI.2017.8126018"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Iman Sharafaldin Arash Habibi\u00a0Lashkari and Ali Ghorbani. 2018. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. 108\u2013116. https:\/\/doi.org\/10.5220\/0006639801080116  Iman Sharafaldin Arash Habibi\u00a0Lashkari and Ali Ghorbani. 2018. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. 108\u2013116. https:\/\/doi.org\/10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"e_1_3_2_1_26_1","unstructured":"Stratosphere. 2015. Stratosphere Laboratory Datasets. https:\/\/www.stratosphereips.org\/datasets-overview [Online; accessed 12-March-2020].  Stratosphere. 2015. Stratosphere Laboratory Datasets. https:\/\/www.stratosphereips.org\/datasets-overview [Online; accessed 12-March-2020]."},{"volume-title":"IEEE Symposium. Computational Intelligence for Security and Defense Applications ([n.\u00a0d.]). https:\/\/doi.org\/10","year":"2009","author":"Tavallaee M.","key":"e_1_3_2_1_27_1"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Sumaiya Thaseen and Aswani\u00a0Kumar Cherukuri. 2016. Intrusion Detection Model Using Chi Square Feature Selection and Modified Na\u00efve Bayes Classifier. Vol.\u00a049. 81\u201391. https:\/\/doi.org\/10.1007\/978-3-319-30348-2_7  Sumaiya Thaseen and Aswani\u00a0Kumar Cherukuri. 2016. Intrusion Detection Model Using Chi Square Feature Selection and Modified Na\u00efve Bayes Classifier. Vol.\u00a049. 81\u201391. https:\/\/doi.org\/10.1007\/978-3-319-30348-2_7","DOI":"10.1007\/978-3-319-30348-2_7"},{"volume-title":"An Intrusion Detection Algorithm Based on Decision Tree Technology. In 2009 Asia-Pacific Conference on Information Processing, Vol.\u00a02. 333\u2013335","year":"2009","author":"Wang J.","key":"e_1_3_2_1_29_1"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2872430"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCNC.2017.7876241"},{"volume-title":"2018 International Conference on Information Networking (ICOIN). 910\u2013913","author":"Yeo M.","key":"e_1_3_2_1_32_1"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2762418"},{"key":"e_1_3_2_1_34_1","first-page":"5","article-title":"Random-Forests-Based Network Intrusion Detection Systems","volume":"38","author":"Zhang J.","year":"2008","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"}],"event":{"name":"7th NSysS 2020: 7th International Conference on Networking, Systems and Security","acronym":"7th NSysS 2020","location":"Dhaka Bangladesh"},"container-title":["7th International Conference on Networking, Systems and Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3428363.3428365","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3428363.3428365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:03Z","timestamp":1750197723000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3428363.3428365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,22]]},"references-count":34,"alternative-id":["10.1145\/3428363.3428365","10.1145\/3428363"],"URL":"https:\/\/doi.org\/10.1145\/3428363.3428365","relation":{},"subject":[],"published":{"date-parts":[[2020,12,22]]}}}