{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T05:28:01Z","timestamp":1769923681595,"version":"3.49.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s10207-025-01171-4","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:39:23Z","timestamp":1765370363000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IoT VisPerNet: Adversarial Perturbation Visualization for IoT Networks"],"prefix":"10.1007","volume":"25","author":[{"given":"Jonathan","family":"Gregory","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob","family":"Auerbach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"issue":"115","key":"1171_CR1","first-page":"782","volume":"186","author":"E Alhajjar","year":"2021","unstructured":"Alhajjar, E., Maxwell, P., Bastian, N.: Adversarial machine learning in network intrusion detection systems. Expert Syst. Appl. 186(115), 782 (2021)","journal-title":"Expert Syst. Appl."},{"key":"1171_CR2","unstructured":"Cloudflare: What is the Mirai Botnet? https:\/\/www.cloudflare.com\/learning\/ddos\/glossary\/mirai-botnet\/, [Online; Accessed 22-January-2025] (2025)"},{"key":"1171_CR3","doi-asserted-by":"crossref","unstructured":"Godinho, P.I.A., Meiguins, B.S., Meiguins, A.S.G., Casseb do Carmo R.M., de Brito Garcia, M., Almeida, L.H., Lourenco, R.: Prisma - a multidimensional information visualization tool using multiple coordinated views. In: 11th International Conference Information Visualization (IV), pp. 23\u201332. Zurich, Switzerland (2007)","DOI":"10.1109\/IV.2007.90"},{"key":"1171_CR4","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations, San Deigo, CA (2015)"},{"key":"1171_CR5","first-page":"1","volume-title":"IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)","author":"J Gregory","year":"2023","unstructured":"Gregory, J., Liao, Q.: Adversarial spam generation using adaptive gradient-based word embedding perturbations. In: IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), pp. 1\u20135. Mt. Pleasant, MI (2023)"},{"key":"1171_CR6","doi-asserted-by":"crossref","unstructured":"Hu, Y., Tian, J., Ma, J., Ying, J.: A novel way to generate adversarial network traffic samples against network traffic classification. Wireless Communications and Mobile Computting (2021)","DOI":"10.1155\/2021\/7367107"},{"key":"1171_CR7","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.eswa.2018.04.004","volume":"106","author":"TY Kim","year":"2018","unstructured":"Kim, T.Y., Cho, S.B.: Web traffic anomaly detection using c-lstm neural networks. Expert Syst. Appl. 106, 66\u201376 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"1171_CR8","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1093\/comjnl\/bxac049","volume":"66","author":"Z Long","year":"2022","unstructured":"Long, Z., Jinsong, W.: Network Traffic Classification Based On A Deep Learning Approach Using NetFlow Data. Comput. J. 66(8), 1882\u20131892 (2022)","journal-title":"Comput. J."},{"key":"1171_CR9","doi-asserted-by":"publisher","unstructured":"Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Network traffic classifier with convolutional and recurrent neural networks for internet of things. IEEE Access 5:18,042\u201318,050, (2017) https:\/\/doi.org\/10.1109\/ACCESS.2017.2747560","DOI":"10.1109\/ACCESS.2017.2747560"},{"key":"1171_CR10","doi-asserted-by":"publisher","unstructured":"Mainuddin, M., Duan, Z., Dong, Y., Salman, S., Taami, T.: Iot device identification based on network traffic characteristics. In: IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, pp 6067\u20136072, (2022) https:\/\/doi.org\/10.1109\/GLOBECOM48099.2022.10001639","DOI":"10.1109\/GLOBECOM48099.2022.10001639"},{"key":"1171_CR11","doi-asserted-by":"publisher","first-page":"3503","DOI":"10.1007\/s10462-021-10088-y","volume":"55","author":"D Minh","year":"2022","unstructured":"Minh, D., Wang, H.X., Li, Y.F., Nguyen, T.N.: Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev. 55, 3503\u20133568 (2022)","journal-title":"Artif. Intell. Rev."},{"issue":"102","key":"1171_CR12","first-page":"994","volume":"72","author":"N Moustafa","year":"2021","unstructured":"Moustafa, N.: A new distributed architecture for evaluating ai-based security systems at the edge: Network ton_iot datasets. Sustain. Cities Soc. 72(102), 994 (2021)","journal-title":"Sustain. Cities Soc."},{"key":"1171_CR13","doi-asserted-by":"crossref","unstructured":"Qazi, E.U.H., Almorjan, A., Zia, T.: A one-dimensional convolutional neural network (1d-cnn) based deep learning system for network intrusion detection. Applied Sciences 12(16) (2022)","DOI":"10.3390\/app12167986"},{"issue":"114","key":"1171_CR14","first-page":"363","volume":"167","author":"X Ren","year":"2021","unstructured":"Ren, X., Gu, H., Wei, W.: Tree-rnn: Tree structural recurrent neural network for network traffic classification. Expert Syst. Appl. 167(114), 363 (2021)","journal-title":"Expert Syst. Appl."},{"key":"1171_CR15","doi-asserted-by":"crossref","unstructured":"Santos, L., Gon\u00e7alves, R., Rabad\u00e3o, C., Martins, J.: A flow-based intrusion detection framework for internet of things networks. Clust. Comput. 26, 37\u201357 (2021)","DOI":"10.1007\/s10586-021-03238-y"},{"issue":"1","key":"1171_CR16","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s11036-021-01843-0","volume":"27","author":"M Sarhan","year":"2022","unstructured":"Sarhan, M., Layeghy, S., Portmann, M.: Towards a standard feature set for network intrusion detection system datasets. Mobile Networks and Applications 27(1), 357\u2013370 (2022). https:\/\/doi.org\/10.1007\/s11036-021-01843-0. (https:\/\/doi.org\/10.1007\/s11036-021-01843-0)","journal-title":"Mobile Networks and Applications"},{"key":"1171_CR17","unstructured":"Sevastjanova, R., Beck, F., Ell, B., Turkay, C., Henkin, R., Butt, M., Keim, D.A., El-Assady, M.: Going beyond visualization : Verbalization as complementary medium to explain machine learning models. In: IEEE VIS Workshop on Visualization for AI Explainability, Berlin, Germany (2018)"},{"key":"#cr-split#-1171_CR18.1","unstructured":"Sinha, S.: State of IoT 2024: Number of connected IoT devices growing 13% to 18.8 billion globally. https:\/\/iot-analytics.com\/number-connected-iot-devices\/, [Online"},{"key":"#cr-split#-1171_CR18.2","unstructured":"Accessed 22-January-2025] (2024)"},{"key":"1171_CR19","doi-asserted-by":"publisher","unstructured":"Siwakoti, Y.R., Bhurtel, M., Rawat, D.B., Oest, A., Johnson, R.C.: Advances in iot security: Vulnerabilities, enabled criminal services, attacks, and countermeasures. IEEE Internet of Things Journal 10(13):11,224\u201311,239, (2023) https:\/\/doi.org\/10.1109\/JIOT.2023.3252594","DOI":"10.1109\/JIOT.2023.3252594"},{"issue":"2","key":"1171_CR20","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s10586-022-03666-4","volume":"26","author":"KM Sudar","year":"2023","unstructured":"Sudar, K.M., Deepalakshmi, P., Singh, A., Srinivasu, P.N.: TFAD: TCP flooding attack detection in software-defined networking using proxy-based and machine learning-based mechanisms. Clust. Comput. 26(2), 1461\u20131477 (2023)","journal-title":"Clust. Comput."},{"key":"1171_CR21","first-page":"1072","volume-title":"International Conference on Trends in Electronics and Informatics (ICEI)","author":"V Vignesh","year":"2017","unstructured":"Vignesh, V., Pavithra, D., Dinakaran, K., Thirumalai, C.: Data analysis using box and whisker plot for stationary shop analysis. In: International Conference on Trends in Electronics and Informatics (ICEI), pp. 1072\u20131076. Tirunelveli, India (2017)"},{"key":"1171_CR22","doi-asserted-by":"publisher","unstructured":"Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: International Conference on Information Networking (ICOIN), Da Nang, Vietnam, pp 712\u2013717, (2017) https:\/\/doi.org\/10.1109\/ICOIN.2017.7899588","DOI":"10.1109\/ICOIN.2017.7899588"},{"key":"1171_CR23","first-page":"263","volume-title":"IEEE International Conference on Integration Technology","author":"Y Xu","year":"2007","unstructured":"Xu, Y., Hong, W., Li, X., Song, J.: Parallel dual visualization of multidimensional multivariate data. In: IEEE International Conference on Integration Technology, pp. 263\u2013268. Shenzhen, China (2007)"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-01171-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-025-01171-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-01171-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T16:07:33Z","timestamp":1769875653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-025-01171-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1171"],"URL":"https:\/\/doi.org\/10.1007\/s10207-025-01171-4","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"27 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"5"}}