{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T10:27:07Z","timestamp":1780050427906,"version":"3.53.1"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100009526","name":"Amrita Vishwa Vidyapeetham University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009526","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009981","name":"Ministry of Coal, Government of India","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009981","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Communications"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1016\/j.comcom.2019.09.014","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T16:37:02Z","timestamp":1569515822000},"page":"215-239","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":96,"special_numbering":"C","title":["VARMAN: Multi-plane security framework for software defined networks"],"prefix":"10.1016","volume":"148","author":[{"given":"Prabhakar","family":"Krishnan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subhasri","family":"Duttagupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishnashree","family":"Achuthan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.comcom.2019.09.014_b1","doi-asserted-by":"crossref","DOI":"10.1145\/1355734.1355746","article-title":"OpenFlow: Enabling innovation in campus networks","author":"McKeown","year":"2008","journal-title":"SIGCOMM Comput. Commun. Rev."},{"issue":"4","key":"10.1016\/j.comcom.2019.09.014_b2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1145\/1282427.1282382","article-title":"Ethane: Taking control of the enter- prise","volume":"37","author":"Casado","year":"2007","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"issue":"2","key":"10.1016\/j.comcom.2019.09.014_b3","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","article-title":"A survey of data mining and machine learning methods for cyber security intrusion detection","volume":"18","author":"Buczak","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"2","key":"10.1016\/j.comcom.2019.09.014_b4","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MNET.2017.1700200","article-title":"Machine learning for networking: Workflow, advances and opportunities","volume":"32","author":"Wang","year":"2018","journal-title":"IEEE Netw."},{"key":"10.1016\/j.comcom.2019.09.014_b5","unstructured":"M. Usama, J. Qadir, A. Raza, H. Arif, K.-L.A. Yau, Y. Elkhatib, A. Hussain, A. Al-Fuqaha, Unsupervised machine learning for networking: Techniques, applications and research challenges, arXiv preprint arXiv:1709.06599, 2017."},{"issue":"1","key":"10.1016\/j.comcom.2019.09.014_b6","first-page":"1","article-title":"Inclusion of artificial intelligence in communication networks and services","author":"Xu","year":"2017","journal-title":"ITU J."},{"issue":"99","key":"10.1016\/j.comcom.2019.09.014_b7","first-page":"1","article-title":"A survey of machine learning techniques applied to self organizing cellular networks","volume":"PP","author":"Klaine","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"4","key":"10.1016\/j.comcom.2019.09.014_b8","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1109\/COMST.2017.2707140","article-title":"State-of-the-art deep learning: Evolving machine intelligence toward tomorrow\u2019s intelligent network traffic control systems","volume":"19","author":"Fadlullah","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.comcom.2019.09.014_b9","unstructured":"E. Hodo, X. Bellekens, A. Hamilton, C. Tachtatzis, R. Atkinson, Shallow and deep networks intrusion detection system: A taxonomy and survey, arXiv preprint arXiv:1701.02145, 2017."},{"key":"10.1016\/j.comcom.2019.09.014_b10","doi-asserted-by":"crossref","unstructured":"Markus Ring, Sarah Wunderlich, Deniz Scheuring, Dieter Landes, Andreas Hotho, A Survey of Network-based Intrusion Detection Data Sets arXiv:1903.02460v1 [cs.CR], 2019.","DOI":"10.1016\/j.cose.2019.06.005"},{"key":"10.1016\/j.comcom.2019.09.014_b11","unstructured":"M. Tavallaee, E. Bagheri, W. Lu, A.A. Ghorbani, Nsl-Kdd Dataset. https:\/\/2.zoppoz.workers.dev:443\/http\/www.unb.ca\/research\/iscx\/dataset\/iscx-NSL-KDD-dataset.html, 2012."},{"key":"10.1016\/j.comcom.2019.09.014_b12","doi-asserted-by":"crossref","unstructured":"I. Sharafaldin, A.H. Lashkari, A.A. Ghorbani, CICIDS2017: Toward Generating a new intrusion detection dataset and intrusion traffic characterization, in: International Conference on Information Systems Security and Privacy (ICISSP), 2018, pp. 108\u2013116.","DOI":"10.5220\/0006639801080116"},{"key":"10.1016\/j.comcom.2019.09.014_b13","unstructured":"P.A.A. Resende, A.C. Drummond, The hogzilla dataset. https:\/\/2.zoppoz.workers.dev:443\/http\/ids-hogzilla.org\/dataset, 2018."},{"key":"10.1016\/j.comcom.2019.09.014_b14","series-title":"International Conference on Ubiquitous Communications and Network Computing UBICNET 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","first-page":"116","article-title":"SDN framework for securing iot networks","author":"Krishnan","year":"2018"},{"key":"10.1016\/j.comcom.2019.09.014_b15","doi-asserted-by":"crossref","unstructured":"Krishnan Prabhakar, Achuthan Krishnashree, Managing network functions in stateful application aware SDN, in: 6th International Symposium on Security in Computing and Communications (2018), Springer Communications in Computer and Information Science Series(CCIS), ISSN: 1865:0929.","DOI":"10.1007\/978-981-13-5826-5_7"},{"key":"10.1016\/j.comcom.2019.09.014_b16","first-page":"151","article-title":"CloudSDN: Enabling SDN framework for security and threat analytics in cloud networks","volume":"276","author":"Krishnan","year":"2019","journal-title":"UBICNET 2019, LNICST"},{"key":"10.1016\/j.comcom.2019.09.014_b17","doi-asserted-by":"crossref","unstructured":"A.R. Curtis, et al. DevoFlow: Scaling flow management for high-perfor- mance networks, in: ACM SIGCOMM, 2011.","DOI":"10.1145\/2018436.2018466"},{"key":"10.1016\/j.comcom.2019.09.014_b18","series-title":"GLOBECOM 2017-2017 IEEE Global Communications Conference","first-page":"1","article-title":"FADM: Ddos flooding attack detection and mitigation system in software-defined networking","author":"Hu","year":"2017"},{"key":"10.1016\/j.comcom.2019.09.014_b19","series-title":"2017 27th International Telecommunication Networks and Applications Conference (ITNAC)","first-page":"1","article-title":"Dynamic attack detection and mitigation in iot using SDN","author":"Bhunia","year":"2017"},{"key":"10.1016\/j.comcom.2019.09.014_b20","doi-asserted-by":"crossref","unstructured":"T. Tang, S.A.R. Zaidi, D. McLernon, L. Mhamdi, M. Ghogho, Deep recurrent neural network for intrusion detection in SDN-based networks, in: Proc. IEEE NetSoft\u201918, Montreal, Canada, 2018.","DOI":"10.1109\/NETSOFT.2018.8460090"},{"key":"10.1016\/j.comcom.2019.09.014_b21","doi-asserted-by":"crossref","unstructured":"T.A. Tuan, L. Mhamdi, D. Mclernon, S.A.R. Zaidi, M. Ghogho, Deep learning approach for network intrusion detection in software defined networking, Int Conf Wirel Netw Mob Commun, https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1109\/WINCOM.2016.7777224, 2016.","DOI":"10.1109\/WINCOM.2016.7777224"},{"key":"10.1016\/j.comcom.2019.09.014_b22","doi-asserted-by":"crossref","unstructured":"S. Choudhury, A. Bhowal, Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection, in: 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM).","DOI":"10.1109\/ICSTM.2015.7225395"},{"key":"10.1016\/j.comcom.2019.09.014_b23","doi-asserted-by":"crossref","unstructured":"M. Anbar, et al. Comparative performance analysis of classification algorithms for intrusion detection system, 14th Annual Conference on Privacy, Security and Trust (PST), 2016.","DOI":"10.1109\/PST.2016.7906975"},{"key":"10.1016\/j.comcom.2019.09.014_b24","doi-asserted-by":"crossref","DOI":"10.1049\/iet-net.2018.5080","article-title":"Towards an efficient anomaly-based intrusion detection for software-defined networks","author":"Latah","year":"2018","journal-title":"IET Netw."},{"key":"10.1016\/j.comcom.2019.09.014_b25","doi-asserted-by":"crossref","unstructured":"M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A.R. Sadeghi, S. Tarkoma, \u2018IoT Sentinel: Automated device-type identification for security enforcement in IoT\u2019. in: Proc. of IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, USA 2017, pp. 2177-2184.","DOI":"10.1109\/ICDCS.2017.283"},{"key":"10.1016\/j.comcom.2019.09.014_b26","series-title":"Proc. of 2017 IEEE Symposium on Computers and Communications (ISCC); 3-6 2017; Heraklion","first-page":"787","article-title":"Flow-based intrusion detection system for SDN","author":"Ajaeiya","year":"2017"},{"key":"10.1016\/j.comcom.2019.09.014_b27","doi-asserted-by":"crossref","unstructured":"C.H. Huang, T.H. Lee, L. Chang, J.R. Lin, G. Horng, \u2018Adversarial attacks on SDN-based deep learning IDS system\u2019. in: Proc. of International Conference on Mobile and Wireless Technology (ICMWT 2018), Hong Kong, China, 2018, pp. 181-191.","DOI":"10.1007\/978-981-13-1059-1_17"},{"key":"10.1016\/j.comcom.2019.09.014_b28","series-title":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","first-page":"195","article-title":"Toward an online anomaly intrusion detection system based on deep learning","author":"Alrawashdeh","year":"2016"},{"key":"10.1016\/j.comcom.2019.09.014_b29","doi-asserted-by":"crossref","unstructured":"Ihsan\u00a0H. Abdulqadder, et al. Deployment of robust security scheme in SDN based 5G network over NFV enabled cloud environment, 2018 IEEE Trans. Emerg. Top. Comput. https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1109\/TETC.2018.2879714.","DOI":"10.1109\/TETC.2018.2879714"},{"key":"10.1016\/j.comcom.2019.09.014_b30","doi-asserted-by":"crossref","unstructured":"F. Junfeng\u00a0Xie, Richard Yu, Tao Huan, Renchao Xie, Jiang Liu, Chenmeng Wang, Yunjie Liu, A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges, IEEE Commun. Surv. Tutor. https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1109\/COMST.2018.2866942.","DOI":"10.1109\/COMST.2018.2866942"},{"key":"10.1016\/j.comcom.2019.09.014_b31","article-title":"Survey on SDN based network intrusion detection system using machine learning approaches","author":"Sultana","year":"2018","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"10.1016\/j.comcom.2019.09.014_b32","doi-asserted-by":"crossref","unstructured":"Tam\u00a0n. Nguyen, The Challenges in ML-based Security for SDN, in: 2018 2nd Cyber Security in Networking Conference (CSNet).","DOI":"10.1109\/CSNET.2018.8602680"},{"issue":"1","key":"10.1016\/j.comcom.2019.09.014_b33","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/COMST.2015.2487361","article-title":"Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges","volume":"18","author":"Qiao","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.comcom.2019.09.014_b34","series-title":"Ifip Wireless and Mobile NETWORKING Conference","first-page":"1","article-title":"Lightweight algorithm for protecting SDN controller against ddos attacks","author":"Christos","year":"2018"},{"issue":"6","key":"10.1016\/j.comcom.2019.09.014_b35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/CC.2017.7961368","article-title":"Sguard:a lightweight SDN safe-guard architecture for DoS attacks","volume":"14","author":"Wang","year":"2017","journal-title":"China Commun."},{"key":"10.1016\/j.comcom.2019.09.014_b36","doi-asserted-by":"crossref","unstructured":". BiaoHan, et al. OverWatch: A cross-plane DDoS attack defense framework with collaborative intelligence in SDN, Hindawi Secur. Commun. Netw. Volume 2018, https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1155\/2018\/9649643.","DOI":"10.1155\/2018\/9649643"},{"key":"10.1016\/j.comcom.2019.09.014_b37","series-title":"SDN-based DDoS Attack Detection with Cross-Plane Collaboration and Lightweight Flow Monitoring","author":"Yang","year":"2017"},{"key":"10.1016\/j.comcom.2019.09.014_b38","doi-asserted-by":"crossref","DOI":"10.1109\/MNET.2015.7113225","article-title":"An extended SDN architecture for network function virtualization with a case study on intrusion prevention","author":"Lin","year":"2015","journal-title":"IEEE Netw."},{"key":"10.1016\/j.comcom.2019.09.014_b39","unstructured":"ONF: https:\/\/2.zoppoz.workers.dev:443\/https\/www.opennetworking.org\/."},{"key":"10.1016\/j.comcom.2019.09.014_b40","doi-asserted-by":"crossref","unstructured":"Andria Procopiou, Nikos Komninos, Christos Douligeris, ForChaos: Real time application DDoS detection using forecasting and chaos theory in smart home iot network, Wirel. Commun. Mob. Comput. Volume 2019, https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1155\/2019\/8469410.","DOI":"10.1155\/2019\/8469410"},{"key":"10.1016\/j.comcom.2019.09.014_b41","doi-asserted-by":"crossref","first-page":"322","DOI":"10.3390\/electronics8030322","article-title":"Features dimensionality reduction approaches for machine learning based network intrusion detection","volume":"8","author":"Abdulhammed","year":"2019","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.comcom.2019.09.014_b42","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","article-title":"A deep learning approach to network intrusion detection","volume":"2","author":"Shone","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.comcom.2019.09.014_b43","doi-asserted-by":"crossref","unstructured":"Ku\u0307bra Kalkan, JESS: Joint entropy based DDoS defense scheme in SDN, IEEE J. Sel. Areas Commun. https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1109\/JSAC.2018.2869997.","DOI":"10.1109\/JSAC.2018.2869997"},{"key":"10.1016\/j.comcom.2019.09.014_b44","doi-asserted-by":"crossref","unstructured":"C. Song, Y. Park, K. Golani, Y. Kim, K. Bhatt, K. Goswami, Machine-learning based threat-aware system in software defined net- works, in: Proc. IEEE ICCCN\u201917, Vancouver, BC, Canada, 2017, pp. 1\u20139.","DOI":"10.1109\/ICCCN.2017.8038436"},{"key":"10.1016\/j.comcom.2019.09.014_b45","doi-asserted-by":"crossref","unstructured":". Hurley, J.\u00a0E. Perdomo, A. Perez-Pons, HMM-based intrusion detection system for software defined networking, in: Proc. IEEE ICMLA\u201916, Anaheim, CA, USA, 2016, pp. 617\u2013621.","DOI":"10.1109\/ICMLA.2016.0108"},{"key":"10.1016\/j.comcom.2019.09.014_b46","doi-asserted-by":"crossref","unstructured":"S. Nanda, F. Zafari, C. DeCusatis, E. Wedaa, B. Yang, Predicting network attack patterns in SDN using machine learning approach, in: Proc. IEEE NFV-SDN\u201916, Palo Alto, CA, USA, 2016, pp. 167\u2013172.","DOI":"10.1109\/NFV-SDN.2016.7919493"},{"key":"10.1016\/j.comcom.2019.09.014_b47","doi-asserted-by":"crossref","DOI":"10.1002\/dac.3497","article-title":"Detection and defense of ddos attack-based on deep learning in openflow- based SDN","author":"Li","year":"2018","journal-title":"Int. J. Commun. Syst."},{"key":"10.1016\/j.comcom.2019.09.014_b48","doi-asserted-by":"crossref","unstructured":"L. Barki, A. Shidling, N. Meti, D.G. Narayan, M.M. Mulla, Detection of distributed denial of service attacks in software defined networks, in: Proc. IEEE ICACCI\u201916, Jaipur, India, 2016, pp. 2576\u20132581.","DOI":"10.1109\/ICACCI.2016.7732445"},{"key":"10.1016\/j.comcom.2019.09.014_b49","unstructured":"A.S. da\u00a0Silva, J.A. Wickboldt, L.Z. Granville, A. Schaeffer-Filho, ATLANTIC: A framework for anomaly traffic detection, classification, and mitigation in SDN, in: Proc. IEEE NOMS\u201916, Istanbul, Turkey, 2016, pp. 27\u201335."},{"key":"10.1016\/j.comcom.2019.09.014_b50","doi-asserted-by":"crossref","first-page":"p65","DOI":"10.1016\/j.jnca.2016.04.005","article-title":"SD-anti-DDoS: Fast and efficient DDoS defense in software-defined networks","volume":"68","author":"Yunhe","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"issue":"12","key":"10.1016\/j.comcom.2019.09.014_b51","first-page":"1","article-title":"\u2018A deep learning based DDoS detection system in software-defined networking (SDN)\u2019","volume":"4","author":"Niyaz","year":"2017","journal-title":"EAI Endorsed Trans. Secur. Safety"},{"key":"10.1016\/j.comcom.2019.09.014_b52","doi-asserted-by":"crossref","unstructured":"T.A. Tang, L. Mhamdi, D. McLernon, S.A.R. Zaidi, M. Ghogho, Deep learning approach for network intrusion detection in software defined networking, in: Proc. IEEE WINCOM\u201916, Fez, Morocco, 2016, pp. 258\u2013263.","DOI":"10.1109\/WINCOM.2016.7777224"},{"key":"10.1016\/j.comcom.2019.09.014_b53","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.cose.2018.04.010","article-title":"Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection","volume":"77","author":"Vijayan\u00a0and","year":"2018","journal-title":"Comput. Secur."},{"key":"10.1016\/j.comcom.2019.09.014_b54","series-title":"International Conference on Cloud Computing and Security","first-page":"322","article-title":"SU-IDS: A semi-supervised and unsupervised framework for network intrusion detection","author":"Min","year":"2018"}],"container-title":["Computer Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0140366419308217?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0140366419308217?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T23:24:14Z","timestamp":1758842654000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0140366419308217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":54,"alternative-id":["S0140366419308217"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.comcom.2019.09.014","relation":{},"ISSN":["0140-3664"],"issn-type":[{"value":"0140-3664","type":"print"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"VARMAN: Multi-plane security framework for software defined networks","name":"articletitle","label":"Article Title"},{"value":"Computer Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.comcom.2019.09.014","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}