Skip to main content
SpringerLink
Log in

Securing Software Defined Networking Using Intrusion Detection System - A Review

  • Conference paper
  • First Online:
Advances in Cyber Security (ACeS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1487))

Included in the following conference series:

Abstract

For the time being, the advances of the Internet technologies in respect of a wide-spread development and the fixed nature of traditional networks have the restricted capacity to satisfy organizational business requirements. Software-Defined Networking (SDN) as a new network architecture presented to overcome these challenges and issues of the existing network topologies and provide peculiar features. However, these programmable and centralized architectures of SDN suffer from new security threats, which require innovative security approaches and techniques such as Intrusion Detection Systems (IDSs). Currently, most of the IDS of SDN are implemented with a machine learning method; however, a deep learning method is also being utilized to satisfy better detection performance. Still, no recent comprehensive review of IDS has been conducted; therefore, this article provides an inclusive and detailed overview and analysis of the SDN with its security issues and attacks and IDS-based on deep learning as a solution for the security issue, to highlight their strengths and weaknesses, and then derive future research directions from these shortcomings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • ONF: Open networking foundation (2014). https://www.opennetworking.org/. Accessed 14 April 2021. n.d. vi, 16

  • Clayman, S., Maini, E., Galis, A., Manzalini, A., Mazzocca, N.: The dynamic placement of virtual network functions. In: 2014 IEEE Network Operations and Management Symposium (NOMS), pp. 1–9. IEEE (2014, May)

    Google Scholar 

  • Mann, V., Vishnoi, A., Kannan, K., Kalyanaraman, S.: CrossRoads: seamless VM mobility across data centers through software defined networking. In: 2012 IEEE Network Operations and Management Symposium, pp. 88–96. IEEE (2012, April)

    Google Scholar 

  • Zhong, H., Fang, Y., Cui, J.: Reprint of LBBSRT: an efficient SDN load balancing scheme based on server response time. Future Gener. Comput. Syst. 80, 409–416 (2018)

    Article  Google Scholar 

  • Ahmad, I., Namal, S., Ylianttila, M., Gurtov, A.: Security in software defined networks: a survey. IEEE Commun. Surv. Tutor. 17(4), 2317–2346 (2015)

    Article  Google Scholar 

  • Sahay, R., Meng, W., Jensen, C.D.: The application of software defined networking on securing computer networks: a survey. J. Netw. Comput. Appl. 131 89–108 (2019)

    Google Scholar 

  • Voellmy, A., Kim, H., Feamster, N.: Procera: a language for high-level reactive network control. In: Proceedings of the first Workshop on Hot Topics in Software Defined Networks, pp. 43–48. (2012, August)

    Google Scholar 

  • Dhamecha, K., Trivedi, B.: SDN issues–a survey. Int. J. Comput. Appl. 73(18), 30–35 (2013)

    Google Scholar 

  • Gude, N., et al.: NOX: towards an operating system for networks. ACM SIGCOMM Comput. Commun. Rev. 38(3) 105–110 (2008a)

    Google Scholar 

  • Todorov, D., Valchanov, H., Aleksieva, V.: Load balancing model based on machine learning and segment routing in SDN. In: 2020 International Conference Automatics and Informatics (ICAI), pp. 1–4. IEEE (2020, October)

    Google Scholar 

  • Kreutz, D., Ramos, F.M., Verissimo, P.E., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2014)

    Article  Google Scholar 

  • Colville, R.J., Spafford, G.: Top Seven Considerations for Configuration Management for Virtual and Cloud Infrastructures (2010). http://img2.insight.com/graphics/no/info2/insight_art6.pdf. Accessed 15 May 2020

  • Voellmy, A., Hudak, P.: Nettle: taking the sting out of programming network routers. In: Rocha R., Launchbury J. (eds.) Practical Aspects of Declarative Languages. PADL 2011. LNCS, vol. 6539, pp. 235–249. Springer, Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18378-2_19

  • Stallings, W.: Software-defined networks and openflow. Internet Protoc. J. 16(1), 2–14 (2013)

    MathSciNet  Google Scholar 

  • Hu, F., Hao, Q., Bao, K.: A survey on software-defined network and openflow: from concept to implementation. IEEE Commun. Surv. Tutor. 16(4), 2181–2206 (2014)

    Article  Google Scholar 

  • Latif, Z., Sharif, K., Li, F., Karim, M.M., Biswas, S., Wang, Y.: A comprehensive survey of interface protocols for software defined networks. J. Netw. Comput. Appl. 156, 102563 (2020)

    Google Scholar 

  • Bahashwan, A.A.O., Manickam, S.: A brief review of messaging protocol standards for internet of things (IoT). J. Cyber Secur. Mobil. 1–14 (2019)

    Google Scholar 

  • Shenker, S., Casado, M., Koponen, T., McKeown, N.: The future of networking, and the past of protocols. Open Netw. Summit 20, 1–30 (2011)

    Google Scholar 

  • Alkhatib, H., et al.: IEEE CS. IEEE Computer (2014)

    Google Scholar 

  • Singh, J., Behal, S.: Detection and mitigation of DDoS attacks in SDN: a comprehensive review, research challenges and future directions. Comput. Sci. Rev. 37, 100279 (2020)

    Google Scholar 

  • Costa, L.C., et al.: OpenFlow data planes performance evaluation. Perform. Eval. 102194 (2021)

    Google Scholar 

  • Casellas, R., Martínez, R., Vilalta, R., Muñoz, R.: Abstraction and control of multi-domain disaggregated optical networks with OpenROADM device models. J. Lightwave Technol. 38(9), 2606–2615 (2020)

    Article  Google Scholar 

  • Benson, T., Akella, A., Maltz, D.A.:. Unraveling the complexity of network management. In: Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, ser. NSDI 2009, Berkeley, CA, USA, 2009, pp. 335–348 (2009)

    Google Scholar 

  • Ahmad, S., Mir, A.H.: Scalability, consistency, reliability and security in SDN controllers: a survey of diverse SDN controllers. J. Netw. Syst. Manag. 29(1), 1–59 (2020). https://doi.org/10.1007/s10922-020-09575-4

    Article  Google Scholar 

  • Stancu, A.L., Halunga, S., Vulpe, A., Suciu, G., Fratu, O., Popovici, E.C.: A comparison between several software defined networking controllers. In: 2015 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), pp. 223–226. IEEE (2015, October)

    Google Scholar 

  • Pradhan, A., Mathew, R.: Solutions to vulnerabilities and threats in software defined networking (SDN). Procedia Comput. Sci. 171, 2581–2589 (2020)

    Article  Google Scholar 

  • Benton, K., Camp, L.J., Small, C.: OpenFlow vulnerability assessment. In: Proceedings of the second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, pp. 151–152 (2013, August)

    Google Scholar 

  • Shalini, S., Vetriselvi, V.: Intrusion detection system for software-defined networks using fuzzy system. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds.) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol. 24, pp. 603–620. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6890-4_59

  • Chakraborti, S., Ray, A.M., Chatterjee, S.R., Chakraborty, M.: Software-defined network vulnerabilities. In: Chakraborty, M., Singh, M., Balas, V.E., Mukhopadhyay, I. (eds.) The “Essence” of Network Security: An End-to-End Panorama. LNNS, vol. 163, pp. 215–239. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9317-8_9

    Chapter  Google Scholar 

  • Chan, R., Tan, F., Teo, U., Kow, B.: Vulnerability assessments of building management systems. In: Staggs, J., Shenoi, S. (eds.) Critical Infrastructure Protection XIV. ICCIP 2020. IFIP Advances in Information and Communication Technology, vol. 596, pp. 209–220. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62840-6_10

  • Shin, S., et al.: Rosemary: a robust, secure, and high-performance network operating system. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 78–89 (2014, November)

    Google Scholar 

  • Mijumbi, R., Serrat, J., Gorricho, J.L., Bouten, N., De Turck, F., Boutaba, R.: Network function virtualization: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 18(1), 236–262 (2015)

    Article  Google Scholar 

  • Chica, J.C.C., Imbachi, J.C., Vega, J.F.B.: Security in SDN: a comprehensive survey. J. Netw. Comput. Appl. 159, 102595 (2020)

    Google Scholar 

  • Chen, X., Mao, Z.M., Van der Merwe, J.: ShadowNet: a platform for rapid and safe network evolution. In: Proceedings of the 2009 Conference on USENIX Annual Technical Conference, p. 3 (2009, June)

    Google Scholar 

  • Shaghaghi, A., Kaafar, M.A., Buyya, R., Jha, S.: Software-defined network (SDN) data plane security: issues, solutions, and future directions. In: Gupta, B., Perez, G., Agrawal, D., Gupta, D. (eds.) Handbook of Computer Networks and Cyber Security, pp. 341–387. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22277-2_14

  • ur Rasool, R., Wang, H., Ashraf, U., Ahmed, K., Anwar, Z., Rafique, W.: A survey of link flooding attacks in software defined network ecosystems. J. Netw. Comput. Appl. 102803 (2020)

    Google Scholar 

  • Abubakar, A., Pranggono, B.: Machine learning based intrusion detection system for software defined networks. In: 2017 seventh International Conference on Emerging Security Technologies (EST), pp. 138–143. IEEE (2017, September)

    Google Scholar 

  • Hamed, T., Ernst, J.B., Kremer, S.C.: A survey and taxonomy of classifiers of intrusion detection systems. In: Daimi, K. (ed.) Computer and Network Security Essentials, pp. 21–39. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58424-9_2

  • Masduki, B.W., Ramli, K., Saputra, F.A., Sugiarto, D.: Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS). In: 2015 International Conference on Quality in Research (QiR), pp. 56–64. IEEE (2015, August)

    Google Scholar 

  • Garg, A., Maheshwari, P.: A hybrid intrusion detection system: a review. In: 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–5. IEEE (2016, January)

    Google Scholar 

  • Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun. Surv. Tutor. 22(3), 1646–1685 (2020)

    Article  Google Scholar 

  • Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., Wahab, A.: A review of intrusion detection systems using machine and deep learning in internet of things: challenges, solutions and future directions. Electronics 9(7), 1177 (2020)

    Article  Google Scholar 

  • Vasilomanolakis, E., Karuppayah, S., Mühlhäuser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. (CSUR) 47(4), 1–33 (2015)

    Article  Google Scholar 

  • Al-Maksousy, H.H., Weigle, M.C., Wang, C.: NIDS: neural network based intrusion detection system. In: 2018 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–6. IEEE (2018, October)

    Google Scholar 

  • Kim, H., Schlansker, M., Santos, J.R., Tourrilhes, J., Turner, Y., Feamster, N.: Coronet: fault tolerance for software defined networks. In: 2012 20th IEEE international conference on network protocols (ICNP), pp. 1–2. IEEE (2012, October)

    Google Scholar 

  • Wanda, P.: A survey of intrusion detection system. Int. J. Inform. Comput. 1(1), 1–10 (2020)

    Google Scholar 

  • Keshk, M., Sitnikova, E., Moustafa, N., Hu, J., Khalil, I.: An integrated framework for privacy-preserving based anomaly detection for cyber-physical systems. In: IEEE Transactions on Sustainable Computing (2019)

    Google Scholar 

  • Vacca, J.R.: Computer and Information Security Handbook Newnes, London (2012)

    Google Scholar 

  • Keshk, M., Turnbull, B., Moustafa, N., Vatsalan, D., Choo, K.K.R.: A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks. IEEE Trans. Ind. Inf. 16(8), 5110–5118 (2020)

    Article  Google Scholar 

  • Liu, C., Yang, J., Chen, R., Zhang, Y., Zeng, J.: Research on immunity-based intrusion detection technology for the Internet of Things. In: 2011 Seventh International Conference on Natural Computation, vol. 1, pp. 212–216). IEEE (2011, July)

    Google Scholar 

  • Kasinathan, P., Pastrone, C., Spirito, M.A., Vinkovits, M.: Denial-of-Service detection in 6LoWPAN based Internet of Things. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 600–607. IEEE (2013a, October)

    Google Scholar 

  • Kasinathan, P., Costamagna, G., Khaleel, H., Pastrone, C., Spirito, M.A.: An IDS framework for internet of things empowered by 6LoWPAN. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 1337–1340 (2013b, November)

    Google Scholar 

  • Keshk, M., Moustafa, N., Sitnikova, E., Turnbull, B.: Privacy-preserving big data analytics for cyber-physical systems. Wirel. Netw. 1–9 (2018). https://doi.org/10.1007/s11276-018-01912-5

  • Mitchell, R., Chen, I.R.: A survey of intrusion detection techniques for cyber-physical systems. ACM Comput. Surv. (CSUR) 46(4), 1–29 (2014)

    Article  Google Scholar 

  • Scarfone, K., Mell, P.: Guide to Intrusion Detection and Prevention Systems (IDPS). Recommendations of the National Institute of Standards and Technology (2007). http://csrc.nist.gov/publications/nistpubs/800-94/SP800-94.pdf

  • McKeown, N., et al.: OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput. Commun. Rev. 38(2), 69–74 (2008b)

    Google Scholar 

  • Amaral, J.P., Oliveira, L.M., Rodrigues, J.J., Han, G., Shu, L.: Policy and network-based intrusion detection system for IPv6-enabled wireless sensor networks. In: 2014 IEEE International Conference on Communications (ICC), pp. 1796–1801. IEEE (2014, June)

    Google Scholar 

  • Butun, I., Morgera, S.D., Sankar, R.: A survey of intrusion detection systems in wireless sensor networks. IEEE Commun. Surv. Tutor. 16(1), 266–282 (2013)

    Article  Google Scholar 

  • Raza, S., Wallgren, L., Voigt, T.: SVELTE: real-time intrusion detection in the Internet of Things. Ad Hoc Netw. 11(8), 2661–2674 (2013)

    Article  Google Scholar 

  • Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M., El Moussa, F.: DeepIDS: deep learning approach for intrusion detection in software defined networking. Electronics 9(9), 1533 (2020)

    Article  Google Scholar 

  • Zhang, C., Wang, X., Li, F., He, Q., Huang, M.: Deep learning–based network application classification for SDN. Trans. Emerg. Telecommun. Technol. 29(5), e3302 (2018)

    Google Scholar 

  • Ujjan, R.M.A., Pervez, Z., Dahal, K., Bashir, A.K., Mumtaz, R., Gonzá-lez, J.: Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN. Future Gener. Comput. Syst. 111, 763–779 (2020)

    Article  Google Scholar 

  • Hu, N., Luan, F., Tian, X., Wu, C.: A novel SDN-based application-awareness mechanism by using deep learning. IEEE Access 8, 160921–160930 (2020)

    Article  Google Scholar 

  • Toupas, P., Chamou, D., Giannoutakis, K.M., Drosou, A., Tzovaras, D.: An intrusion detection system for multi-class classification based on deep neural networks. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1253–1258. IEEE (2019a, December)

    Google Scholar 

  • Ravi, R.: A performance analysis of Software Defined Network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA). Comput. Commun. 153, 375–381 (2020)

    Article  Google Scholar 

  • Assis, M.V., Carvalho, L.F., Lloret, J., Proença Jr., M.L.: A GRU deep learning system against attacks in software defined networks. J. Netw. Comput. Appl. 177, 102942 (2021)

    Google Scholar 

  • Enns, R., Bjorklund, M., Schoenwaelder, J., Bierman, A.: Rfc 6241, network configuration protocol (netconf). Internet Engineering Task Force (IETF) (June 2011)

    Google Scholar 

  • Elsayed, M.S., Le-Khac, N.A., Dev, S., Jurcut, A.D.: Ddosnet: a deep-learning model for detecting network attacks. In: 2020 IEEE 21st International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 391–396. IEEE (2020, August)

    Google Scholar 

  • Nguyen, M.T., Kim, K.: Genetic convolutional neural network for intrusion detection systems. Future Gener. Comput. Syst. 113, 418–427 (2020)

    Article  Google Scholar 

  • Zhang, H., Huang, L., Wu, C.Q., Li, Z.: An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset (2020)

    Google Scholar 

  • Garg, S., Kaur, K., Kumar, N., Rodrigues, J.J.: Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in SDN: a social multimedia perspective. IEEE (2019)

    Google Scholar 

  • Zhang, Y., Chen, X., Guo, D., Song, M., Teng, Y., Wang, X.: PCCN: parallel cross convolutional neural network for abnormal network traffic flows detection in multi-class imbalanced network traffic flows. IEEE Ac-Cess 7, 119904–119916 (2019a)

    Article  Google Scholar 

  • Qin, Y., Wei, J., Yang, W.: Deep learning based anomaly detection scheme in software-defined networking. In: 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4. IEEE (2019, September)

    Google Scholar 

  • Zhang, X., Ran, J., Mi, J.: An intrusion detection system based on convolutional neural network for imbalanced network traffic. In: 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), pp. 456–460. IEEE (2019b, October)

    Google Scholar 

  • Boukria, S., Guerroumi, M.: Intrusion detection system for SDN network using deep learning approach. In: 2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), vol. 1, pp. 1–6. IEEE (2019, December)

    Google Scholar 

  • Su, T., Sun, H., Zhu, J., Wang, S., Li, Y.: BAT: deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access 8, 29575–29585 (2020)

    Article  Google Scholar 

  • Doria, A., et al.: Forwarding and control element separation (ForCES) protocol specification. RFC 5810, 1–124 (2010)

    Google Scholar 

  • Azizjon, M., Jumabek, A., Kim, W.: 1D CNN based net-work intrusion detection with normalization on imbalanced data. In: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 218–224. IEEE (2020, February)

    Google Scholar 

  • Malik, J., Akhunzada, A., Bibi, I., Imran, M., Musaddiq, A., Kim, S.W.: Hybrid deep learning: an efficient reconnaissance and surveillance (2020a)

    Google Scholar 

  • Lee, T.H., Chang, L.H., Syu, C.W.: Deep learning enabled intrusion detection and prevention system over SDN networks. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6. IEEE (2020, June)

    Google Scholar 

  • Al-E’mari, S., Anbar, M., Sanjalawe, Y., Manickam, S.: A labeled transactions-based dataset on the ethereum network. In: Anbar, M., Abdullah, N., Manickam, S. (eds.) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol. 1347, pp. 61–79. Springer, Singapore (2020). https://doi.org/10.1007/978-981-33-6835-4_5

  • Kaur, G., Lashkari, A.H., Rahali, A.: Intrusion traffic de-tection and characterization using deep image learning. In: 2020 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), pp. 55–62. IEEE (2020, August)

    Google Scholar 

  • Pakanzad, S.N., Monkaresi, H.: Providing a hybrid approach for detecting malicious traffic on the computer networks using convolutional neural networks. In: 2020 28th Iranian Conference on Electrical Engineering (ICEE), pp. 1–6. IEEE (2020, August)

    Google Scholar 

  • Sinha, J., Manollas, M.: Efficient deep CNN-BiLSTM model for network intrusion detection. In: Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition, pp. 223–231 (2020, June)

    Google Scholar 

  • Mhamdi, L., McLernon, D., El-moussa, F., Zaidi, S.A. R., Ghogho, M., Tang, T.: A deep learning approach combining auto-encoder with one-class SVM for DDoS attack detection in SDNs. In: 2020 IEEE Eighth International Conference on Communications and Networking (ComNet) (2021)

    Google Scholar 

  • Ahn, S., Kim, J., young Park, S., Cho, S.: Explaining deep learning-based traffic classification using a genetic algorithm. IEEE Access (2020)

    Google Scholar 

  • Bai, X., Bai, J., Yang, X., Liu, H., Wang, B., Liu, Y.: A deep learning approach to detect anomaly in software-defined network. In: 2020 12th International Conference on Advanced Infocomm Technology (ICAIT), pp. 100–106. IEEE (2020, November)

    Google Scholar 

  • Dinh, P.T., Park, M.: R-EDoS: robust economic denial of sustainability detection in an SDN-based cloud through stochastic recurrent neural network. IEEE Access 9, 35057–35074 (2021)

    Article  Google Scholar 

  • Tootoonchian, A., Gorbunov, S., Ganjali, Y., Casado, M., Sherwood, R.: On controller performance in software-defined networks. In: 2nd {USENIX} Workshop on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services (Hot-ICE 12) (2012)

    Google Scholar 

  • Khan, R.U., Zhang, X., Alazab, M., Kumar, R.: An improved convolutional neural network model for intrusion detection in networks. In: 2019 Cybersecurity and Cyberforensics Conference (CCC), pp. 74–77. IEEE (2019, May)

    Google Scholar 

  • Zhang, Z., Ma, L., Poularakis, K., Leung, K.K., Tucker, J., Swami, A.: Macs: deep reinforcement learning based sdn controller synchronization policy design. In: 2019 IEEE 27th International Conference on Network Protocols (ICNP), pp. 1–11. IEEE (2019, October)

    Google Scholar 

  • Shu, J., Zhou, L., Zhang, W., Du, X., Guizani, M.: Collaborative intrusion detection for VANETs: a deep learning-based distributed SDN approach. IEEE Transactions on Intelligent Transportation Systems (2020)

    Google Scholar 

  • Xiao, Y., Xing, C., Zhang, T., Zhao, Z.: An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access 7, 42210–42219 (2019)

    Article  Google Scholar 

  • Subba, B.: A Neural Network based NIDS framework for intrusion detection in contemporary network traffic. In: 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–6. IEEE (2019, December)

    Google Scholar 

  • Wang, P., Ye, F., Chen, X., Qian, Y.: Datanet: deep learning based encrypted network traffic classification in SDN home gateway. IEEE Access 6, 55380–55391 (2018)

    Article  Google Scholar 

  • Malik, J., Akhunzada, A., Bibi, I., Imran, M., Musaddiq, A., Kim, S.W.: Hybrid Deep Learning: An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN (2020b)

    Google Scholar 

  • Aladaileh, M.A., Anbar, M., Hasbullah, I.H., Chong, Y.W., Sanjalawe, Y.K.: Detection techniques of distributed denial of service attacks on software-defined networking controller–a review. IEEE Access 8, 143985–143995 (2020)

    Article  Google Scholar 

  • Alanazi, S. T., Anbar, M., Karuppayah, S., Al-Ani, A.K., Sanjalawe, Y.K.: Detection techniques for DDoS attacks in cloud environment. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds.) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol. 67, pp. 337–354. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6031-2_34

  • Mohammad, A., Mohammed, A., Hasbullah, H., Yousef, S.: Information theory-based approaches to detect DDoS attacks on software-defined networking controller a review. Int. J. Educ. Inf. Technol. 5, 83–94 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Advanced IPv6 Centre of Excellence (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, Malaysia

    Noor Al-Mi’ani, Mohammed Anbar, Yousef Sanjalawe & Shankar Karuppayah

Authors
  1. Noor Al-Mi’ani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Anbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yousef Sanjalawe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shankar Karuppayah
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  2. Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

  3. Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al-Mi’ani, N., Anbar, M., Sanjalawe, Y., Karuppayah, S. (2021). Securing Software Defined Networking Using Intrusion Detection System - A Review. In: Abdullah, N., Manickam, S., Anbar, M. (eds) Advances in Cyber Security. ACeS 2021. Communications in Computer and Information Science, vol 1487. Springer, Singapore. https://doi.org/10.1007/978-981-16-8059-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8059-5_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8058-8

  • Online ISBN: 978-981-16-8059-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation