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Abstract
In recent years, networking systems have witnessed a breakthrough. Due to the advances in network and communication technology, new concepts such as the Internet of Things (IoT) have emerged in network configurations. The IoT framework connects devices such as computers, mobile phones, etc. Hence, there is a necessity to secure networks. One of the possible tools to secure such networks is the Intrusion Detection Systems (IDS). The idea of operation of these systems depends on anomaly detection tools and concepts. In this paper, we mainly concentrate on the Software-Defined Network (SDN) implementation of IoT networks. This implementation saves the large cost of many hardware components in the networks. The paper is targeted to the application of IDS in SDN-based IoT networks. Different studies are presented, explored, and compared. The main objective of this survey study is to introduce new research directions for SDN-based IoT networks. Moreover, the new concept of blockchain is exploited in this paper for security enhancement of SDN-based IoT networks.
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References
Ahmed, R. A., Hemdan, E. E. D., El‐Shafai, W., Ahmed, Z. A., El‐Rabaie, E. S. M., & Abd El‐Samie, F. E. Climate‐smart agriculture using intelligent techniques, blockchain and Internet of Things: Concepts, challenges, and opportunities. Transactions on Emerging Telecommunications Technologies, p. e4607.
El-Shafai, W., Khallaf, F., El-Rabaie, E. S. M., El-Samie, A., & Fathi, E. (2022). Proposed neural SAE-based medical image cryptography framework using deep extracted features for smart IoT healthcare applications. Neural Computing and Applications, pp. 1–25.
ElShafee, A., & El-Shafai, W. (2022). Design and analysis of data link impersonation attack for wired LAN application layer services. Journal of Ambient Intelligence and Humanized Computing, pp. 1–24.
ZAVRAK, S., & İskefiyeli, M. (2022). Flow-Based Intrusion Detection on Software-Defined Networks: A Multivariate Time Series Anomaly Detection Approach.
Samrin, R., & Vasumathi, D. (2017). Review on anomaly based network intrusion detection system. In 2017 international conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT) (pp. 141–147). IEEE.
Alshammri, G. H., Samha, A. K., Hemdan, E. E. D., Amoon, M., & El-Shafai, W. (2022). An efficient intrusion detection framework in software-defined networking for cybersecurity applications.CMC-COMPUTERS MATERIALS & CONTINUA,72(2), 3529–3548.
Schaller, S., & Hood, D. (2017). Software defined networking architecture standardization.Computer standards & interfaces,54, 197–202.
Almomani, I., Alkhayer, A., & El-Shafai, W. (2022). A crypto-steganography approach for hiding ransomware within HEVC streams in android IoT devices.Sensors,22(6), 2281.
Thanigaivelan, N.K., Nigussie, E., Kanth, R.K., Virtanen, S., Isoaho, J.: Distributed internal anomaly detection system for internet-of-things. In 13th IEEE Annual Consumer Communications Networking Conference (CCNC), pp. 319–320 (2016)
Zahra, F., Jhanjhi, N. Z., Brohi, S. N., Khan, N. A., Masud, M., & AlZain, M. A. (2022). Rank and wormhole attack detection model for RPL-based internet of things using machine learning.Sensors,22(18), 6765.
Siam, A. I., Almaiah, M. A., Al-Zahrani, A., Elazm, A. A., El Banby, G. M., El-Shafai, W., ... & El-Bahnasawy, N. A. (2021). Secure health monitoring communication systems based on IoT and cloud computing for medical emergency applications. Computational Intelligence and Neuroscience, 2021.
Alessandro, S., Felix, G., Mauro, C., & Jens-Matthias, B. (2016). Raspberry pi ids: A fruitful intrusion detection system for iot. In 2017 13th IEEE International Conference on Advanced and Trusted Computing (ATC 2016) (pp. 1–9).
Khalil, A. A., E Ibrahim, F., Abbass, M. Y., Haggag Mahrous, N. Y., Sedik, A., & Abd El-Samie, F. E. (2022). Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems.International Journal for Numerical Methods in Biomedical Engineering,38(1), e3530.
Hadi, M. R., & Mohammed, A. S. (2022). A novel approach to network intrusion detection system using deep learning for Sdn: Futuristic approach. arXiv preprintarXiv:2208.02094.
Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches.Peer-to-Peer Networking and Applications,12(2), 493–501.
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system.Ieee Access,7, 41525–41550.
Khanbhai, M., Anyadi, P., Symons, J., Flott, K., Darzi, A., & Mayer, E. (2021). Applying natural language processing and machine learning techniques to patient experience feedback: A systematic review. BMJ Health & Care Informatics, 28(1).
Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C., & Atkinson, R. (2017). Shallow and deep networks intrusion detection system: A taxonomy and survey. arXiv preprintarXiv:1701.02145.
Samkria, R., Abd-Elnaby, M., Singh, R., Gehlot, A., Rashid, M., Aly, M. H., & El-Shafai, W. (2021). Automatic PV grid fault detection system with IoT and LabVIEW as data logger.Comput. Mater. Contin,69, 1709–1723.
Hassan, H. A., Hemdan, E. E., El-Shafai, W., Shokair, M., & Abd El-Samie, F. E. (2021). An Efficient Intrusion Detection System for SDN using Convolutional Neural Network. In 2021 International Conference on Electronic Engineering (ICEEM) (pp. 1–5). IEEE.
El-Shafai, W., Fawzi, A., Zekry, A., Abd El-Samie, F. E., & Abd-Elnaby, M. (2021). Spectrum measurement and utilization in an outdoor 5-GHz Wi-Fi network using cooperative cognitive radio system.International Journal of Communication Systems,34(10), e4774.
Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system.Eai Endorsed Transactions on Security and Safety,3(9), e2.
Taher, K. A., Jisan, B. M. Y., & Rahman, M. M. (2019). Network intrusion detection using supervised machine learning technique with feature selection. In 2019 International conference on robotics, electrical and signal processing techniques (ICREST) (pp. 643–646). IEEE.
Akram, S. V., Singh, R., AlZain, M. A., Gehlot, A., Rashid, M., Faragallah, O. S., & Prashar, D. (2021). Performance analysis of iot and long-range radio-based sensor node and gateway architecture for solid waste management.Sensors,21(8), 2774.
Alkasassbeh, M., & Almseidin, M. (2018). Machine learning methods for network intrusion detection. arXiv preprintarXiv:1809.02610.
Ding, S., Zhu, Z., & Zhang, X. (2017). An overview on semi-supervised support vector machine.Neural Computing and Applications,28(5), 969–978.
Alarifi, A., Sankar, S., Altameem, T., Jithin, K. C., Amoon, M., & El-Shafai, W. (2020). A novel hybrid cryptosystem for secure streaming of high efficiency H. 265 compressed videos in IoT multimedia applications.IEEE Access,8, 128548–128573.
Halimaa, A., & Sundarakantham, K. (2019). Machine learning based intrusion detection system. In 2019 3rd International conference on trends in electronics and informatics (ICOEI) (pp. 916–920). IEEE.
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks.Ieee Access,5, 21954–21961.
Çavuşoğlu, Ü. (2019). A new hybrid approach for intrusion detection using machine learning methods.Applied Intelligence,49(7), 2735–2761.
Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection.IEEE transactions on emerging topics in computational intelligence,2(1), 41–50.
Lin, W. H., Lin, H. C., Wang, P., Wu, B. H., & Tsai, J. Y. (2018). Using convolutional neural networks to network intrusion detection for cyber threats. In 2018 IEEE International Conference on Applied System Invention (ICASI) (pp. 1107–1110). IEEE.
Ingre, B., & Yadav, A. (2015). Performance analysis of NSL-KDD dataset using ANN. In 2015 international conference on signal processing and communication engineering systems (pp. 92–96). IEEE.
Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. In 2016 international conference on platform technology and service (PlatCon) (pp. 1–5). IEEE.
Divekar, A., Parekh, M., Savla, V., Mishra, R., & Shirole, M. (2018). Benchmarking datasets for anomaly-based network intrusion detection: KDD CUP 99 alternatives. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) (pp. 1–8). IEEE.
Sang-Hyun, C., & Hee-Su, C. (2014). Feature Selection using Attribute Ratio in NSL-KDD data. In International Conference Data Mining, Civil and Mechanical Engineering (ICDMCME’2014) (pp. 90–92).
A Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection.IEEE Communications surveys & tutorials,18(2), 1153–1176.
González-Abad, J., García, Á. L., & Kozlov, V. Y. (2022). A container-based workflow for distributed training of deep learning algorithms in HPC Clusters. arXiv preprintarXiv:2208.02498.
Bengio, Y., Lecun, Y., & Hinton, G. (2021). Deep learning for AI.Communications of the ACM,64(7), 58–65.
Nanni, L., Manfè, A., Maguolo, G., Lumini, A., & Brahnam, S. (2022). High performing ensemble of convolutional neural networks for insect pest image detection.Ecological Informatics,67, 101515.
Ahmad, J., Farman, H., & Jan, Z. (2019). Deep learning methods and applications. In Deep learning: convergence to big data analytics (pp. 31–42). Springer.
Alom, M. Z., Bontupalli, V., & Taha, T. M. (2015). Intrusion detection using deep belief networks. In 2015 National Aerospace and Electronics Conference (NAECON) (pp. 339–344). IEEE.
Dhillon, A., & Verma, G. K. (2020). Convolutional neural network: A review of models, methodologies and applications to object detection.Progress in Artificial Intelligence,9(2), 85–112.
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. IEEE Computational intelligenCe magazine, 13(3), 55–75.
Shetty, S. M., Shirahatti, H., Patil, U., & Deepak, K. T. (2022). Voice Activity Detection Through Adversarial Learning. In 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) (pp. 163–166). IEEE.
Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., & Razaque, A. (2020). Deep recurrent neural network for IoT intrusion detection system.Simulation Modelling Practice and Theory,101, 102031.
Phil, K. (2017).Matlab deep learning with machine learning, neural networks and artificial intelligence. Apress.
Bakhshi, T. (2017). State of the art and recent research advances in software defined networking. Wireless Communications and Mobile Computing, 2017.
Yan, Q., Yu, F. R., Gong, Q., & Li, J. (2015). Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges.IEEE communications surveys & tutorials,18(1), 602–622.
Siddique, K., Akhtar, Z., Khan, F. A., & Kim, Y. (2019). KDD cup 99 data sets: A perspective on the role of data sets in network intrusion detection research.Computer,52(2), 41–51.
Protić, D. D. (2018). Review of KDD Cup ‘99, NSL-KDD and Kyoto 2006+ datasets.Vojnotehnički glasnik/Military Technical Courier,66(3), 580–596.
Xu, C., Qin, D., & Song, F. (2022). A Survey of SDN Traffic Management Research. In 2022 11th International Conference on Communications, Circuits and Systems (ICCCAS) (pp. 231–236). IEEE.
Scott-Hayward, S., Natarajan, S., & Sezer, S. (2015). A survey of security in software defined networks.IEEE Communications Surveys & Tutorials,18(1), 623–654.
Sood, K., Yu, S., & Xiang, Y. (2015). Software-defined wireless networking opportunities and challenges for Internet-of-Things: A review.IEEE Internet of Things Journal,3(4), 453–463.
Guo, X., & Tang, B. (2022). Security Threats and Countermeasures for Software-Defined Internet of Things. In International Conference on Artificial Intelligence and Security (pp. 654–662). Springer.
Jararweh, Y., Al-Ayyoub, M., Darabseh, A., Benkhelifa, E., Vouk, M., & Rindos, A. (2015). SDIoT: A software defined based internet of things framework.Journal of Ambient Intelligence and Humanized Computing,6(4), 453–461.
Liu, J., Li, Y., Chen, M., Dong, W., & Jin, D. (2015). Software-defined internet of things for smart urban sensing.IEEE communications magazine,53(9), 55–63.
Salman, O., Abdallah, S., Elhajj, I. H., Chehab, A., & Kayssi, A. (2016). Identity-based authentication scheme for the Internet of Things. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 1109–1111). IEEE.
Chakrabarty, S., Engels, D. W., & Thathapudi, S. (2015). Black SDN for the Internet of Things. In 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems (pp. 190–198). IEEE.
Theodorou, T., Violettas, G., Valsamas, P., Petridou, S., & Mamatas, L. (2019). A multi-protocol software-defined networking solution for the Internet of Things.IEEE Communications Magazine,57(10), 42–48.
Tran, A. K., Piran, M. J., & Pham, C. (2019). SDN controller placement in IoT networks: An optimized submodularity-based approach.Sensors,19(24), 5474.
Molina Zarca, A., Garcia-Carrillo, D., Bernal Bernabe, J., Ortiz, J., Marin-Perez, R., & Skarmeta, A. (2019). Enabling virtual AAA management in SDN-based IoT networks.Sensors,19(2), 295.
Lu, Y., Ling, Z., Zhu, S., & Tang, L. (2017). SDTCP: Towards datacenter TCP congestion control with SDN for IoT applications.Sensors,17(1), 109.
Zhang, A., & Lin, X. (2017). Security-aware and privacy-preserving D2D communications in 5G.IEEE Network,31(4), 70–77.
Ahmed, M. E., & Kim, H. (2017). DDoS attack mitigation in Internet of Things using software defined networking. In 2017 IEEE third international conference on big data computing service and applications (BigDataService) (pp. 271–276). IEEE.
Li, C., Qin, Z., Novak, E., & Li, Q. (2017). Securing SDN infrastructure of IoT–fog networks from MitM attacks.IEEE Internet of Things Journal,4(5), 1156–1164.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications.IEEE communications surveys & tutorials,17(4), 2347–2376.
Pohrmen, F. H., Das, R. K., Khongbuh, W., & Saha, G. (2018). Blockchain-based security aspects in Internet of Things network. In International Conference on Advanced Informatics for Computing Research (pp. 346–357). Springer, Singapore.
Pohrmen, F. H., Das, R. K., & Saha, G. (2019). Blockchain-based security aspects in heterogeneous Internet-of-Things networks: A survey.Transactions on Emerging Telecommunications Technologies,30(10), e3741.
Li, W., He, M., & Haiquan, S. (2021). An overview of blockchain technology: applications, challenges and future trends. In 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC) 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC) (pp. 31–39). IEEE.
Salman, O., Elhajj, I., Chehab, A., & Kayssi, A. (2018). IoT survey: An SDN and fog computing perspective.Computer Networks,143, 221–246.
Dorri, A., Kanhere, S. S., & Jurdak, R. (2016). Blockchain in internet of things: challenges and solutions. arXiv preprintarXiv:1608.05187.
Sharma, P. K., Singh, S., Jeong, Y. S., & Park, J. H. (2017). Distblocknet: A distributed blockchains-based secure sdn architecture for iot networks.IEEE Communications Magazine,55(9), 78–85.
Sharma, P. K., Chen, M. Y., & Park, J. H. (2017). A software defined fog node based distributed blockchain cloud architecture for IoT.Ieee Access,6, 115–124.
ElSayed, M. S., Le-Khac, N. A., Albahar, M. A., & Jurcut, A. (2021). A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique.Journal of Network and Computer Applications,191, 103160.
Isa, M. M., & Mhamdi, L. (2020). Native SDN intrusion detection using machine learning. In 2020 IEEE Eighth International Conference on Communications and Networking (ComNet) (pp. 1–7). IEEE.
Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks.IEEE Access,7, 42210–42219.
Elsayed, M. S., Le-Khac, N. A., Dev, S., & Jurcut, A. D. (2020). Detecting abnormal traffic in large-scale networks. In 2020 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1–7). IEEE.
Said Elsayed, M., Le-Khac, N. A., Dev, S., & Jurcut, A. D. (2020). Network anomaly detection using LSTM based autoencoder. In Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (pp. 37–45).
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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
Heba A. Hassan, Walid El-Shafai, Mona Shokair & Fathi E. Abd El-Samie
Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
Ezz E. Hemdan
Security Engineering Laboratory, Department of Computer Science, Prince Sultan University, Riyadh, 11586, Saudi Arabia
Walid El-Shafai
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
Fathi E. Abd El-Samie
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Hassan, H.A., Hemdan, E.E., El-Shafai, W.et al. Intrusion Detection Systems for the Internet of Thing: A Survey Study.Wireless Pers Commun128, 2753–2778 (2023). https://doi.org/10.1007/s11277-022-10069-6
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