{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:04:01Z","timestamp":1783613041178,"version":"3.55.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NSF Grants","award":["2113945"],"award-info":[{"award-number":["2113945"]}]},{"name":"NSF Grants","award":["2200538"],"award-info":[{"award-number":["2200538"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In today\u2019s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI\/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software.<\/jats:p>","DOI":"10.1186\/s42400-023-00170-z","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T03:01:46Z","timestamp":1701486106000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine learning based fileless malware traffic classification using image visualization"],"prefix":"10.1186","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4600-0298","authenticated-orcid":false,"given":"Fikirte Ayalke","family":"Demmese","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajaya","family":"Neupane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajad","family":"Khorsandroo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"May","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaushik","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"170_CR1","first-page":"265","volume":"16","author":"M Abadi","year":"2016","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. Osdi 16:265\u2013283","journal-title":"Osdi"},{"key":"170_CR2","doi-asserted-by":"publisher","unstructured":"Abdullayeva F (2019) Malware detection in cloud computing using an image visualization technique. In: 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT), pp 1\u20135. IEEE, https:\/\/doi.org\/10.1109\/AICT47866.2019.8981727","DOI":"10.1109\/AICT47866.2019.8981727"},{"key":"170_CR3","unstructured":"Babar FM (2020) Emerging & unconventional malware detection using a hybrid approach. PhD thesis, University of Windsor (Canada)"},{"key":"170_CR4","unstructured":"Barnes E (2021) Fileless attacks: addressing evolving malware threats. https:\/\/www.infosecurity-magazine.com\/opinions\/fileless-attacks-malware\/ Accessed Accessed 19 Oct 2022"},{"key":"170_CR5","unstructured":"Barut O, Luo Y, Zhang T, Li W, Li P (2020) Netml: a challenge for network traffic analytics. 1, 13006, arXiv preprint arXiv:2004.13006"},{"key":"170_CR6","doi-asserted-by":"crossref","unstructured":"Borana P, Sihag V, Choudhary G, Vardhan M, Singh P (2021) An assistive tool for fileless malware detection. In: 2021 World Automation Congress (WAC), pp 21\u201325","DOI":"10.23919\/WAC50355.2021.9559449"},{"key":"170_CR7","doi-asserted-by":"crossref","unstructured":"Bozkir AS, Cankaya AO, Aydos M (2019) Utilization and comparision of convolutional neural networks in malware recognition. In: 2019 27th Signal Processing and Communications Applications Conference (SIU), pp 1\u20134","DOI":"10.1109\/SIU.2019.8806511"},{"key":"170_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102166","volume":"103","author":"AS Bozkir","year":"2021","unstructured":"Bozkir AS, Tahillioglu E, Aydos M, Kara I (2021) Catch them alive: a malware detection approach through memory forensics, manifold learning and computer vision. Comput Secur 103:102166","journal-title":"Comput Secur"},{"key":"170_CR9","unstructured":"Bressert E (2012) SciPy and NumPy: an Overview for Developers. \u201c O\u2019Reilly Media, Inc.\u201d, ISBN: 9781449361624"},{"key":"170_CR10","doi-asserted-by":"crossref","unstructured":"Bucevschi AG, Balan G, Prelipcean DB (2019) Preventing file-less attacks with machine learning techniques. In: 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp 248\u2013252","DOI":"10.1109\/SYNASC49474.2019.00042"},{"key":"170_CR11","doi-asserted-by":"crossref","unstructured":"Chukka A, Devi V (2021) Detection of malicious binaries by deep learning methods. International Conference on Internet of Things, vol 2021. Big Data and Security, IoTBDS-Proceedings. Science and Technology Publications, Lda, N\/A, pp 132\u2013139","DOI":"10.5220\/0010379701320139"},{"key":"170_CR12","unstructured":"Culjak I, Abram D, Pribanic T, Dzapo H, Cifrek M (2012) A brief introduction to opencv. In: 2012 Proceedings of the 35th International Convention MIPRO, pp 1725\u20131730"},{"key":"170_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.diin.2018.09.006","volume":"27","author":"Y Dai","year":"2018","unstructured":"Dai Y, Li H, Qian Y, Lu X (2018) A malware classification method based on memory dump grayscale image. Digit Investig 27:30\u201337","journal-title":"Digit Investig"},{"key":"170_CR14","doi-asserted-by":"crossref","unstructured":"Dhote Y, Agrawal S, Deen AJ (2015) A survey on feature selection techniques for internet traffic classification. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN), pp 1375\u20131380","DOI":"10.1109\/CICN.2015.267"},{"key":"170_CR15","unstructured":"Fang V (2018) Malicious PowerShell Detection via Machine Learning. https:\/\/www.mandiant.com\/resources\/blog\/malicious-powershell-detection-via-machine-learning Accessed Accessed 22 Oct 2022"},{"issue":"1","key":"170_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11416-018-0323-0","volume":"15","author":"D Gibert","year":"2019","unstructured":"Gibert D, Mateu C, Planes J, Vicens R (2019) Using convolutional neural networks for classification of malware represented as images. J Comput Virol Hack Tech 15(1):15\u201328","journal-title":"J Comput Virol Hack Tech"},{"key":"170_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1450\/1\/012075","volume":"1450","author":"W Handaya","year":"2020","unstructured":"Handaya W, Yusoff M, Jantan A (2020) Machine learning approach for detection of fileless cryptocurrency mining malware. J Phys Conf Ser 1450:012075","journal-title":"J Phys Conf Ser"},{"key":"170_CR18","doi-asserted-by":"crossref","unstructured":"Hendler D, Kels S, Rubin A (2018) Detecting malicious powershell commands using deep neural networks. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security, pp 187\u2013197","DOI":"10.1145\/3196494.3196511"},{"issue":"1","key":"170_CR19","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","volume":"195","author":"DH Hubel","year":"1968","unstructured":"Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195(1):215\u2013243","journal-title":"J Physiol"},{"issue":"3","key":"170_CR20","first-page":"1","volume":"21","author":"H Joo","year":"2021","unstructured":"Joo H, Choi H, Yun C, Cheon M (2021) Efficient network traffic classification and visualizing abnormal part via hybrid deep learning approach: Xception+ bidirectional gru. Glob J Comput Sci Technol 21(3):1\u201310","journal-title":"Glob J Comput Sci Technol"},{"key":"170_CR21","doi-asserted-by":"crossref","unstructured":"Kancherla K, Mukkamala S (2013) Image visualization based malware detection. In: 2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), pp 40\u201344","DOI":"10.1109\/CICYBS.2013.6597204"},{"key":"170_CR22","doi-asserted-by":"crossref","unstructured":"Ketkar N (2017) Introduction to keras. In: Deep Learning with Python, Springer, pp 97\u2013111","DOI":"10.1007\/978-1-4842-2766-4_7"},{"issue":"2","key":"170_CR23","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3390\/s23020612","volume":"23","author":"O Khalid","year":"2023","unstructured":"Khalid O, Ullah S, Ahmad T, Saeed S, Alabbad DA, Aslam M, Buriro A, Ahmad R (2023) An insight into the machine-learning-based fileless malware detection. Sensors 23(2):612","journal-title":"Sensors"},{"key":"170_CR24","doi-asserted-by":"crossref","unstructured":"Khorsandroo S, Tosun AS (2018) Time inference attacks on software defined networks: Challenges and countermeasures. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp 342\u2013349","DOI":"10.1109\/CLOUD.2018.00050"},{"key":"170_CR25","doi-asserted-by":"crossref","unstructured":"Khorsandroo S, Tosun AS (2019) White box analysis at the service of low rate saturation attacks on virtual sdn data plane. In: 2019 IEEE 44th LCN Symposium on Emerging Topics in Networking (LCN Symposium), pp 100\u2013107","DOI":"10.1109\/LCNSymposium47956.2019.9000660"},{"key":"170_CR26","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"16","key":"170_CR27","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.3390\/electronics10162042","volume":"10","author":"J Krupski","year":"2021","unstructured":"Krupski J, Graniszewski W, Iwanowski M (2021) Data transformation schemes for cnn-based network traffic analysis: a survey. Electronics 10(16):2042","journal-title":"Electronics"},{"issue":"1","key":"170_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-019-0043-x","volume":"3","author":"S Kumar","year":"2020","unstructured":"Kumar S et al (2020) An emerging threat fileless malware: a survey and research challenges. Cybersecurity 3(1):1\u201312","journal-title":"Cybersecurity"},{"key":"170_CR29","doi-asserted-by":"crossref","unstructured":"Kumar A, Sagar KP, Kuppusamy K, Aghila G (2016) Machine learning based malware classification for android applications using multimodal image representations. In: 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp 1\u20136","DOI":"10.1109\/ISCO.2016.7726949"},{"issue":"20","key":"170_CR30","doi-asserted-by":"publisher","first-page":"30743","DOI":"10.1007\/s11042-020-10165-4","volume":"80","author":"P Li","year":"2021","unstructured":"Li P, Tang H, Yu J, Song W (2021) Lstm and multiple cnns based event image classification. Multimed Tools Appl 80(20):30743\u201330760","journal-title":"Multimed Tools Appl"},{"issue":"12","key":"170_CR31","doi-asserted-by":"publisher","first-page":"2550","DOI":"10.3390\/app9122550","volume":"9","author":"H-K Lim","year":"2019","unstructured":"Lim H-K, Kim J-B, Kim K, Hong Y-G, Han Y-H (2019) Payload-based traffic classification using multi-layer lstm in software defined networks. Appl Sci 9(12):2550","journal-title":"Appl Sci"},{"key":"170_CR32","doi-asserted-by":"crossref","unstructured":"Liu J, Zhang X, Zhang J, An J, Li C, Gao L (2018) Hyperspectral image classification based on long short term memory network. In: 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), pp 1\u20135","DOI":"10.1109\/EORSA.2018.8598645"},{"key":"170_CR33","unstructured":"Mudge R (2018) Malleable-C2-Profiles. https:\/\/github.com\/rsmudge\/Malleable-C2-Profiles"},{"key":"170_CR34","unstructured":"Mudge R (2019) Cobalt Strike: Beware of Slow Downloads. https:\/\/www.cobaltstrike.com\/blog\/beware-of-slow-downloads\/ Accessed 18 Apr 2023"},{"key":"170_CR35","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.compeleceng.2019.03.015","volume":"76","author":"H Naeem","year":"2019","unstructured":"Naeem H, Guo B, Naeem MR, Ullah F, Aldabbas H, Javed MS (2019) Identification of malicious code variants based on image visualization. Comput Electr Eng 76:225\u2013237","journal-title":"Comput Electr Eng"},{"key":"170_CR36","doi-asserted-by":"crossref","unstructured":"Nataraj L, Karthikeyan S, Jacob G, Manjunath BS (2011) Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security, pp 1\u20137","DOI":"10.1145\/2016904.2016908"},{"issue":"4","key":"170_CR37","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/SURV.2008.080406","volume":"10","author":"TT Nguyen","year":"2008","unstructured":"Nguyen TT, Armitage G (2008) A survey of techniques for internet traffic classification using machine learning. IEEE Commun Surv Tutor 10(4):56\u201376","journal-title":"IEEE Commun Surv Tutor"},{"key":"170_CR38","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1016\/j.cose.2018.04.005","volume":"77","author":"S Ni","year":"2018","unstructured":"Ni S, Qian Q, Zhang R (2018) Malware identification using visualization images and deep learning. Comput Secur 77:871\u2013885","journal-title":"Comput Secur"},{"key":"170_CR39","unstructured":"Rahman A (2021) Cobalt Strike: Defining Cobalt Strike Components & BEACON. https:\/\/www.mandiant.com\/resources\/blog\/defining-cobalt-strike-components Accessed 05 Oct 2022"},{"key":"170_CR40","doi-asserted-by":"crossref","unstructured":"Ran J, Chen Y, Li S (2018) Three-dimensional convolutional neural network based traffic classification for wireless communications. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp 624\u2013627","DOI":"10.1109\/GlobalSIP.2018.8646659"},{"key":"170_CR41","doi-asserted-by":"crossref","unstructured":"Rigaki M, Garcia S (2018) Bringing a gan to a knife-fight: Adapting malware communication to avoid detection. In: 2018 IEEE Security and Privacy Workshops (SPW), pp 70\u201375","DOI":"10.1109\/SPW.2018.00019"},{"key":"170_CR42","first-page":"11","volume":"5","author":"S Saad","year":"2019","unstructured":"Saad S, Briguglio W, Elmiligi H (2019) The curious case of machine learning in malware detection. Mach Learn Interpret Malware Detect 5:11","journal-title":"Mach Learn Interpret Malware Detect"},{"key":"170_CR43","doi-asserted-by":"crossref","unstructured":"Saad S, Mahmood F, Briguglio W, Elmiligi H (2019) Jsless: A tale of a fileless javascript memory-resident malware. In: International Conference on Information Security Practice and Experience. Springer, pp 113\u2013131","DOI":"10.1007\/978-3-030-34339-2_7"},{"key":"170_CR44","doi-asserted-by":"crossref","unstructured":"Saleh I, Ji H (2020) Network traffic images: A deep learning approach to the challenge of internet traffic classification. In: 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), pp 0329\u20130334","DOI":"10.1109\/CCWC47524.2020.9031260"},{"key":"170_CR45","doi-asserted-by":"crossref","unstructured":"Sanjay B, Rakshith D, Akash R, Hegde VV (2018) An approach to detect fileless malware and defend its evasive mechanisms. In: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), pp 234\u2013239","DOI":"10.1109\/CSITSS.2018.8768769"},{"key":"170_CR46","unstructured":"Seazzu L (2016) Cobalt strike 3.0. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States)"},{"issue":"1","key":"170_CR47","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.istr.2009.03.003","volume":"14","author":"A Shabtai","year":"2009","unstructured":"Shabtai A, Moskovitch R, Elovici Y, Glezer C (2009) Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Inf Secur Tech Rep 14(1):16\u201329","journal-title":"Inf Secur Tech Rep"},{"key":"170_CR48","doi-asserted-by":"crossref","unstructured":"Shapira T, Shavitt Y (2019) Flowpic: encrypted internet traffic classification is as easy as image recognition. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp 680\u2013687","DOI":"10.1109\/INFCOMW.2019.8845315"},{"issue":"1","key":"170_CR49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"A Sharma","year":"2019","unstructured":"Sharma A, Vans E, Shigemizu D, Boroevich KA, Tsunoda T (2019) Deepinsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Sci Rep 9(1):1\u20137","journal-title":"Sci Rep"},{"key":"170_CR50","unstructured":"Smelcer J (2017) Rise of fileless malware. PhD thesis, Utica College"},{"key":"170_CR51","doi-asserted-by":"crossref","unstructured":"Su J, Vasconcellos DV, Prasad S, Sgandurra D, Feng Y, Sakurai K (2018) Lightweight classification of iot malware based on image recognition. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol 2, pp 664\u2013669","DOI":"10.1109\/COMPSAC.2018.10315"},{"key":"170_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102538","volume":"154","author":"H Tahaei","year":"2020","unstructured":"Tahaei H, Afifi F, Asemi A, Zaki F, Anuar NB (2020) The rise of traffic classification in iot networks: A survey. J Netw Comput Appl 154:102538","journal-title":"J Netw Comput Appl"},{"issue":"4","key":"170_CR53","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/bdcc2040037","volume":"2","author":"S Taheri","year":"2018","unstructured":"Taheri S, Salem M, Yuan J-S (2018) Leveraging image representation of network traffic data and transfer learning in botnet detection. Big Data Cogn Comput 2(4):37","journal-title":"Big Data Cogn Comput"},{"key":"170_CR54","doi-asserted-by":"crossref","unstructured":"Taud H, Mas J (2018) Multilayer perceptron (mlp). In: Geomatic Approaches for Modeling Land Change Scenarios. Springer, pp 451\u2013455","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"170_CR55","unstructured":"van\u00a0der Eijk V, Schuijt C (2020) Detecting cobalt strike beacons in netflow data. Technical report, University of Amsterdam"},{"key":"170_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107138","volume":"171","author":"D Vasan","year":"2020","unstructured":"Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q (2020) Imcfn: Image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171:107138","journal-title":"Comput Netw"},{"key":"170_CR57","doi-asserted-by":"crossref","unstructured":"Wang W, Zhu M, Zeng X, Ye X, Sheng Y (2017) Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), pp 712\u2013717","DOI":"10.1109\/ICOIN.2017.7899588"},{"issue":"1","key":"170_CR58","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1109\/COMST.2018.2866942","volume":"21","author":"J Xie","year":"2018","unstructured":"Xie J, Yu FR, Huang T, Xie R, Liu J, Wang C, Liu Y (2018) A survey of machine learning techniques applied to software defined networking (sdn): Research issues and challenges. IEEE Commun Surv Tutor 21(1):393\u2013430","journal-title":"IEEE Commun Surv Tutor"},{"key":"170_CR59","doi-asserted-by":"crossref","unstructured":"Xu P, Eckert C, Zarras A (2021) Falcon: malware detection and categorization with network traffic images. In: International Conference on Artificial Neural Networks, pp 117\u2013128","DOI":"10.1007\/978-3-030-86362-3_10"},{"issue":"4","key":"170_CR60","first-page":"230","volume":"9","author":"B Yadav","year":"2021","unstructured":"Yadav B, Tokekar S (2021) Recent innovations and comparison of deep learning techniques in malware classification: a review. Int J Inf Secur Sci 9(4):230\u2013247","journal-title":"Int J Inf Secur Sci"},{"issue":"1","key":"170_CR61","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1109\/TPDS.2012.98","volume":"24","author":"J Zhang","year":"2012","unstructured":"Zhang J, Xiang Y, Wang Y, Zhou W, Xiang Y, Guan Y (2012) Network traffic classification using correlation information. IEEE Trans Parallel Distrib Syst 24(1):104\u2013117","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"170_CR62","doi-asserted-by":"crossref","unstructured":"Zhang J, Qin Z, Yin H, Ou L, Hu Y (2016) Irmd: malware variant detection using opcode image recognition. In: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), pp 1175\u20131180","DOI":"10.1109\/ICPADS.2016.0155"},{"key":"170_CR63","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhao S, Zhang J, Ma X, Huang F (2019) Stnn: A novel tls\/ssl encrypted traffic classification system based on stereo transform neural network. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp 907\u2013910","DOI":"10.1109\/ICPADS47876.2019.00133"},{"key":"170_CR64","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.009","volume":"72","author":"J Zhao","year":"2021","unstructured":"Zhao J, Jing X, Yan Z, Pedrycz W (2021) Network traffic classification for data fusion: a survey. Inf Fusion 72:22\u201347","journal-title":"Inf Fusion"},{"issue":"1","key":"170_CR65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Brettin T, Xia F, Partin A, Shukla M, Yoo H, Evrard YA, Doroshow JH, Stevens RL (2021) Converting tabular data into images for deep learning with convolutional neural networks. Sci Rep 11(1):1\u201311","journal-title":"Sci Rep"}],"container-title":["Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-023-00170-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42400-023-00170-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-023-00170-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T03:06:29Z","timestamp":1701486389000},"score":1,"resource":{"primary":{"URL":"https:\/\/cybersecurity.springeropen.com\/articles\/10.1186\/s42400-023-00170-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["170"],"URL":"https:\/\/doi.org\/10.1186\/s42400-023-00170-z","relation":{},"ISSN":["2523-3246"],"issn-type":[{"value":"2523-3246","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"13 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"32"}}