Skip to main content
SpringerLink
Log in

Deep Learning in IoT Intrusion Detection

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

The Internet of Things (IoT) is the new paradigm of our times, where smart devices and sensors from across the globe are interconnected in a global grid, and distributed applications and services impact every area of human activity. With its huge economic impact and its pervasive influence over our lives, IoT is an attractive target for criminals, and cybersecurity becomes a top priority for the IoT ecosystem. Although cybersecurity has been the subject of research for decades, the large-scale IoT architecture and the emergence of novel threats render old strategies largely inefficient. Deep learning may provide cutting edge solutions for IoT intrusion detection, with its data-driven, anomaly-based approach and ability to detect emerging, unknown attacks. This survey offers a detailed review of deep learning models that have been proposed for IoT intrusion detection. Solutions have been classified by model in a comprehensive, structured analysis of how deep learning has been applied for IoT cybersecurity and their unique contributions to the development of effective IoT intrusion detection solutions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Abdullah, M., Alshannaq, A., Balamash, A., Almabdy, S.: Enhanced intrusion detection system using feature selection method and ensemble learning algorithms. IJCSIS 16(2), 48–55 (2018)

    Google Scholar 

  2. Abeshu, A., Chilamkurti, N.: Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun. Mag. 56(2), 169–175 (2018). https://doi.org/10.1109/MCOM.2018.1700332

    Article  Google Scholar 

  3. Abomhara, M., Køien, G.M.: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. J. Cyber Secur. Mobil. 4(1), 65–88 (2015)

    Article  Google Scholar 

  4. Agarwal, N., Hussain, S.Z.: A closer look at intrusion detection system for web applications. Secur. Commun. Netw. (2018)

  5. Al-Hawawreh, M., Moustafa, N., Sitnikova, E.: Identification of malicious activities in industrial internet of things based on deep learning models. J. Inf. Secur. Appl. 41, 1–11 (2018). https://doi.org/10.1016/j.jisa.2018.05.002

    Article  Google Scholar 

  6. Al Jallad, K., Aljnidi, M., Desouki, M.S.: Anomaly detection optimization using big data and deep learning to reduce false-positive. J. Big Data 7(1), 1–12 (2020)

    Article  Google Scholar 

  7. Al-Jarrah, O.Y., Maple, C., Dianati, M., Oxtoby, D., Mouzakitis, A.: Intrusion detection systems for intra-vehicle networks: a review. IEEE Access 7, 21266–21289 (2019). https://doi.org/10.1109/ACCESS.2019.2894183

    Article  Google Scholar 

  8. Alaiz-Moreton, H., Aveleira-Mata, J., Ondicol-Garcia, J., Muñoz-Castañeda, A.L., García, I., Benavides, C.: Multiclass classification procedure for detecting attacks on MQTT-IoT protocol. Complexity 2019, 1–11 (2019). https://doi.org/10.1155/2019/6516253

    Article  Google Scholar 

  9. Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., Razaque, A.: Deep recurrent neural network for IoT intrusion detection system. Simul. Model. Pract. Theory 101, 102031 (2020). https://doi.org/10.1016/j.simpat.2019.102031

    Article  Google Scholar 

  10. Aloqaily, M., Otoum, S., Ridhawi, I.A., Jararweh, Y.: An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw. 90, 101842 (2019). https://doi.org/10.1016/j.adhoc.2019.02.001

    Article  Google Scholar 

  11. Amanullah, M.A., Habeeb, R.A.A., Nasaruddin, F.H., Gani, A., Ahmed, E., Nainar, A.S.M., Akim, N.M., Imran, M.: Deep learning and big data technologies for IoT security. Comput. Commun. 151, 495–517 (2020)

    Article  Google Scholar 

  12. Bahşi, H., Nõmm, S., La Torre, F.B.: Dimensionality reduction for machine learning based IoT botnet detection. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1857–1862. IEEE (2018)

  13. Bassey, J., Adesina, D., Li, X., Qian, L., Aved, A., Kroecker, T.: Intrusion detection for IoT devices based on RF fingerprinting using deep learning. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), pp. 98–104. IEEE (2019)

  14. Belenko, V., Chernenko, V., Kalinin, M., Krundyshev, V.: Evaluation of GAN applicability for intrusion detection in self-organizing networks of cyber physical systems. In: 2018 International Russian Automation Conference (RusAutoCon), pp. 1–7. IEEE (2018)

  15. Bengio, Y.: Learning deep architectures for AI, the essence of knowledge, vol. 2, no. 1, 2009. Now, Boston and Delft (2009). http://www.nowpublishers.com/product.aspx?product=MAL&doi=220000000

  16. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  17. Chollet, F., et al.: Deep Learning with Python, vol. 361. Manning, New York (2018)

    Google Scholar 

  18. Chowdhury, M.M.U., Hammond, F., Konowicz, G., Xin, C., Wu, H., Li, J.: A few-shot deep learning approach for improved intrusion detection. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 456–462. IEEE (2017)

  19. Darbandi, F., Jafari, A., Karimipour, H., Dehghantanha, A., Derakhshan, F., Choo, K.K.R.: Real-time stability assessment in smart cyber-physical grids: a deep learning approach. IET Smart Grid 3(4), 454–461 (2020)

    Article  Google Scholar 

  20. Dawoud, A., Shahristani, S., Raun, C.: Deep learning and software-defined networks: towards secure IoT architecture. Internet of Things 3, 82–89 (2018)

    Article  Google Scholar 

  21. Diro, A., Chilamkurti, N.: Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun. Mag. 56(9), 124–130 (2018). https://doi.org/10.1109/MCOM.2018.1701270

    Article  Google Scholar 

  22. Diro, A.A., Chilamkurti, N.: Distributed attack detection scheme using deep learning approach for Internet of Things. Fut. Gener. Comput. Syst. 82, 761–768 (2018)

    Article  Google Scholar 

  23. Elsaeidy, A., Munasinghe, K.S., Sharma, D., Jamalipour, A.: Intrusion detection in smart cities using restricted Boltzmann machines. J. Netw. Comput. Appl. 135, 76–83 (2019)

    Article  Google Scholar 

  24. Eswari, T., Vanitha, V.: A novel rule based intrusion detection framework for Wireless Sensor Networks. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 1019–1022. IEEE, Chennai (2013). 10.1109/ICICES.2013.6508172

  25. Ferdowsi, A., Saad, W.: Generative adversarial networks for distributed intrusion detection in the internet of things. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2019)

  26. Fernandes, E., Rahmati, A., Eykholt, K., Prakash, A.: Internet of things security research: a rehash of old ideas or new intellectual challenges? IEEE Secur. Priv. 15(4), 79–84 (2017)

    Article  Google Scholar 

  27. Ferrag, M.A., Maglaras, L., Ahmim, A., Derdour, M., Janicke, H.: RDTIDS: rules and decision tree-based intrusion detection system for internet-of-things networks. Fut. Internet 12(3), 44 (2020)

    Article  Google Scholar 

  28. Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020)

    Google Scholar 

  29. Fiore, U., Palmieri, F., Castiglione, A., De Santis, A.: Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122, 13–23 (2013)

    Article  Google Scholar 

  30. Garcia-Morchon, O., Kumar, S., Keoh, S., Hummen, R., Struik, R.: Security considerations in the IP-based Internet of Things draft-garciacore-security-06. Internet Engineering Task Force (2013)

  31. Gül, A., Adalı, E.: A feature selection algorithm for ids. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 816–820. IEEE (2017)

  32. HaddadPajouh, H., Dehghantanha, A., Khayami, R., Choo, K.K.R.: A deep recurrent neural network based approach for Internet of Things malware threat hunting. Fut. Gener. Comput. Syst. 85, 88–96 (2018). https://doi.org/10.1016/j.future.2018.03.007

    Article  Google Scholar 

  33. Hasan, M., Islam, M.M., Zarif, M.I.I., Hashem, M.: Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things 7, 100059 (2019). https://doi.org/10.1016/j.iot.2019.100059

    Article  Google Scholar 

  34. He, Y., Mendis, G.J., Wei, J.: Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans. Smart Grid 8(5), 2505–2516 (2017). https://doi.org/10.1109/TSG.2017.2703842

    Article  Google Scholar 

  35. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006). https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  36. Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.L., Iorkyase, E., Tachtatzis, C., Atkinson, R.: In: Threat analysis of IoT networks using artificial neural network intrusion detection system, pp. 1–6. IEEE (2016)

  37. Hu, W., Liao, Y., Vemuri, V.R.: Robust support vector machines for anomaly detection in computer security. In: ICMLA, pp. 168–174 (2003)

  38. Huda, S., Miah, S., Yearwood, J., Alyahya, S., Al-Dossari, H., Doss, R.: A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network. J. Parallel Distrib. Comput. 120, 23–31 (2018)

    Article  Google Scholar 

  39. Huong, P.V., Thuan, L.D., Hong Van, L.T., Hung, D.V.: Intrusion detection in IoT systems based on deep learning using convolutional neural network. In: 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), pp. 448–453. IEEE, Hanoi, Vietnam (2019). https://doi.org/10.1109/NICS48868.2019.9023871. https://ieeexplore.ieee.org/document/9023871/

  40. Hussain, F., Hussain, R., Hassan, S.A., Hossain, E.: Machine learning in IoT security: current solutions and future challenges. IEEE Commun. Surv. Tutor. 22(3), 1686–1721 (2020). https://doi.org/10.1109/COMST.2020.2986444

    Article  Google Scholar 

  41. Hwang, R.H., Peng, M.C., Huang, C.W.: Detecting IoT malicious traffic based on autoencoder and convolutional neural network. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE (2019)

  42. Hwang, R.H., Peng, M.C., Nguyen, V.L., Chang, Y.L.: An LSTM-based deep learning approach for classifying malicious traffic at the packet level. Appl. Sci. 9(16), 3414 (2019). https://doi.org/10.3390/app9163414

    Article  Google Scholar 

  43. Ibitoye, O., Shafiq, O., Matrawy, A.: Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2019)

  44. Imamverdiyev, Y.N., Abdullayeva, F.J.: Deep learning in cybersecurity: challenges and approaches. IJCWT 10(2), 82–105 (2020)

    Google Scholar 

  45. Jan, T.: Ada-boosted locally enhanced probabilistic neural network for IoT intrusion detection. In: Conference on Complex, Intelligent, and Software Intensive Systems, pp. 583–589. Springer (2018)

  46. Jun, C., Chi, C.: Design of complex event-processing IDS in internet of things. In: 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation, pp. 226–229. IEEE (2014)

  47. Kang, M.J., Kang, J.W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016)

    Article  Google Scholar 

  48. Karimipour, H., Dehghantanha, A., Parizi, R.M., Choo, K.K.R., Leung, H.: A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80778–80788 (2019)

    Article  Google Scholar 

  49. Khan, M.A., Karim, M., Kim, Y., et al.: A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry 11(4), 583 (2019)

    Article  Google Scholar 

  50. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1), 1–22 (2019)

    Article  Google Scholar 

  51. King, J., Awad, A.I.: A distributed security mechanism for resource-constrained IoT devices. Informatica 40(1), 133 (2016)

    Google Scholar 

  52. Kingma, D.P., Mohamed, S., Jimenez Rezende, D., Welling, M.: Semi-supervised learning with deep generative models. Adv. Neural Inf. Process. Syst. 27, 3581–3589 (2014)

    Google Scholar 

  53. Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: Advances in Neural Information Processing Systems, pp. 971–980 (2017)

  54. Kobojek, P., Saeed, K.: Application of recurrent neural networks for user verification based on keystroke dynamics. J. Telecommun. Inf. Technol. 2016(3), 80–90 (2016)

    Google Scholar 

  55. Kolias, C., Kambourakis, G., Stavrou, A., Voas, J.: DDoS in the IoT: Mirai and other botnets. Computer 50(7), 80–84 (2017)

    Article  Google Scholar 

  56. Kulkarni, R.V., Venayagamoorthy, G.K.: Neural network based secure media access control protocol for wireless sensor networks. In: 2009 International Joint Conference on Neural Networks, pp. 1680–1687. IEEE (2009)

  57. Lee, J., Park, K.: GAN-Based Imbalanced Data Intrusion Detection System. Personal and Ubiquitous Computing, pp. 1–8. Springer, Berlin (2019)

    Google Scholar 

  58. Lee, S.J., Yoo, P.D., Asyhari, A.T., Jhi, Y., Chermak, L., Yeun, C.Y., Taha, K.: IMPACT: impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access 8, 65520–65529 (2020)

    Article  Google Scholar 

  59. Li, D., Deng, L., Lee, M., Wang, H.: IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. Int. J. Inf. Manage 49, 533–545 (2019)

    Article  Google Scholar 

  60. Li, J., Zhao, Z., Li, R., Zhang, H.: Ai-based two-stage intrusion detection for software defined IoT networks. IEEE Internet of Things J. 6(2), 2093–2102 (2018)

    Article  Google Scholar 

  61. Li, W., Yi, P., Wu, Y., Pan, L., Li, J.: A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J. Electric. Comput. Eng. (2014) 10.1155/2014/240217

  62. Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20), 4396 (2019). https://doi.org/10.3390/app9204396

    Article  Google Scholar 

  63. Liu, J., Yin, L., Hu, Y., Lv, S., Sun, L.: A novel intrusion detection algorithm for industrial control systems based on CNN and process state transition. In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2018)

  64. Liu, Y., Pi, D.: A novel kernel SVM algorithm with game theory for network intrusion detection. KSII Trans. Internet Inf. Syst. 11(8), 20 (2017)

    Google Scholar 

  65. Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT. Sensors 17(9), 1967 (2017)

    Article  Google Scholar 

  66. Loukas, G., Vuong, T., Heartfield, R., Sakellari, G., Yoon, Y., Gan, D.: Cloud-based cyber-physical intrusion detection for vehicles using deep learning. IEEE Access 6, 3491–3508 (2017)

    Article  Google Scholar 

  67. Ma, T., Wang, F., Cheng, J., Yu, Y., Chen, X.: A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks. Sensors 16(10), 1701 (2016). https://doi.org/10.3390/s16101701

    Article  Google Scholar 

  68. Madhawa, S., Balakrishnan, P., Arumugam, U.: Roll forward validation based decision tree classification for detecting data integrity attacks in industrial internet of things. J. Intell. Fuzzy Syst. 36(3), 2355–2366 (2019). https://doi.org/10.3233/JIFS-169946

    Article  Google Scholar 

  69. Manimurugan, S., Al-Mutairi, S., Aborokbah, M.M., Chilamkurti, N., Ganesan, S., Patan, R.: Effective attack detection in internet of medical things smart environment using a deep belief neural network. IEEE Access 8, 77396–77404 (2020). https://doi.org/10.1109/ACCESS.2020.2986013

    Article  Google Scholar 

  70. McDermott, C.D., Majdani, F., Petrovski, A.V.: Botnet detection in the internet of things using deep learning approaches. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, Rio de Janeiro (2018). 10.1109/IJCNN.2018.8489489. https://ieeexplore.ieee.org/document/8489489/

  71. McElwee, S., Heaton, J., Fraley, J., Cannady, J.: Deep learning for prioritizing and responding to intrusion detection alerts. In: MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM), pp. 1–5. IEEE (2017)

  72. Mehmood, A., Mukherjee, M., Ahmed, S.H., Song, H., Malik, K.M.: NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. J. Supercomput. 74(10), 5156–5170 (2018). https://doi.org/10.1007/s11227-018-2413-7

    Article  Google Scholar 

  73. Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., Elovici, Y.: N-BaIoT-network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12–22 (2018). https://doi.org/10.1109/MPRV.2018.03367731

    Article  Google Scholar 

  74. Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)

  75. Min, E., Long, J., Liu, Q., Cui, J., Chen, W.: TR-IDS: anomaly-based intrusion detection through text-convolutional neural network and random forest. Secur. Commun. Netw. (2018)

  76. Moustafa, N., Slay, J.: Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE (2015)

  77. Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019)

    Article  Google Scholar 

  78. Munir, M., Siddiqui, S.A., Dengel, A., Ahmed, S.: DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991–2005 (2019). https://doi.org/10.1109/ACCESS.2018.2886457

    Article  Google Scholar 

  79. Münz, G., Li, S., Carle, G.: Traffic anomaly detection using k-means clustering. In: GI/ITG Workshop MMBnet, pp. 13–14 (2007)

  80. Ngo, Q.D., Nguyen, H.T., Nguyen, L.C., Nguyen, D.H.: A survey of IoT malware and detection methods based on static features. ICT Express (2020)

  81. Nicolae, M.I., Sinn, M., Tran, M.N., Buesser, B., Rawat, A., Wistuba, M., Zantedeschi, V., Baracaldo, N., Chen, B., Ludwig, H., et al.: Adversarial Robustness Toolbox v1. 0.0. arXiv preprint arXiv:1807.01069 (2018)

  82. Notra, S., Siddiqi, M., Gharakheili, H.H., Sivaraman, V., Boreli, R.: An experimental study of security and privacy risks with emerging household appliances. In: 2014 IEEE conference on communications and network security, pp. 79–84. IEEE (2014)

  83. O’Shea, T.J., West, N., Vondal, M., Clancy, T.C.: Semi-supervised radio signal identification. In: 2017 19th International Conference on Advanced Communication Technology (ICACT), pp. 33–38. IEEE, Pyeongchang, Kwangwoon Do, South Korea (2017). https://doi.org/10.23919/ICACT.2017.7890052. http://ieeexplore.ieee.org/document/7890052/

  84. Otoum, S., Kantarci, B., Mouftah, H.T.: On the feasibility of deep learning in sensor network intrusion detection. IEEE Netw. Lett. 1(2), 68–71 (2019)

    Article  Google Scholar 

  85. Pamukov, M.E., Poulkov, V.K., Shterev, V.A.: Negative selection and neural network based algorithm for intrusion detection in IoT. In: 2018 41st International Conference on Telecommunications and Signal Processing (TSP), pp. 1–5. IEEE (2018)

  86. Pan, J., Yang, Z.: Cybersecurity Challenges and Opportunities in the New” Edge Computing+ IoT” World. In: Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization, pp. 29–32 (2018)

  87. Patel, K.K., Patel, S.M., et al.: Internet of things-iot: definition, characteristics, architecture, enabling technologies, application & future challenges. Int. J. Eng. Sci. Comput. 6(5), (2016)

  88. Potluri, S., Ahmed, S., Diedrich, C.: Convolutional neural networks for multi-class intrusion detection system. In: International Conference on Mining Intelligence and Knowledge Exploration, pp. 225–238. Springer (2018)

  89. Prabavathy, S., Sundarakantham, K., Shalinie, S.M.: Design of cognitive fog computing for intrusion detection in Internet of Things. J. Commun. Netw. 20(3), 291–298 (2018)

    Article  Google Scholar 

  90. Prokofiev, A.O., Smirnova, Y.S., Surov, V.A.: A method to detect Internet of Things botnets. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 105–108. IEEE (2018)

  91. Qureshi, A.u.H., Larijani, H., Ahmad, J., Mtetwa, N.: A Heuristic intrusion detection system for Internet-of-Things (IoT). In: K. Arai, R. Bhatia, S. Kapoor (eds.) Intelligent Computing, vol. 997, pp. 86–98. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-22871-2_7. http://link.springer.com/10.1007/978-3-030-22871-2_7. Series Title: Advances in Intelligent Systems and Computing

  92. Radford, B.J., Apolonio, L.M., Trias, A.J., Simpson, J.A.: Network traffic anomaly detection using recurrent neural networks. arXiv preprint arXiv:1803.10769 (2018)

  93. Rigaki, M., Garcia, S.: Bringing a gan to a knife-fight: adapting malware communication to avoid detection. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 70–75. IEEE (2018)

  94. Ring, M., Wunderlich, S., Gruedl, D., Landes, D., Hotho, A.: Technical report cidds-001 data set. Tech. rep., Tech. rep. 2017 (cit. on p. 19) (2018)

  95. Roux, J., Alata, E., Auriol, G., Nicomette, V., Kaâniche, M.: Toward an intrusion detection approach for IoT based on radio communications profiling. In: 2017 13th European Dependable Computing Conference (EDCC), pp. 147–150. IEEE (2017)

  96. Saeed, A., Ahmadinia, A., Javed, A., Larijani, H.: Random neural network based intelligent intrusion detection for wireless sensor networks. Proc. Comput. Sci. 80, 2372–2376 (2016)

    Article  Google Scholar 

  97. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  98. Sethi, P., Sarangi, S.R.: Internet of things: architectures, protocols, and applications. J. Electr. Comput. Eng. (2017)

  99. Sfar, A.R., Natalizio, E., Challal, Y., Chtourou, Z.: A roadmap for security challenges in the Internet of Things. Digital Commun. Netw. 4(2), 118–137 (2018)

    Article  Google Scholar 

  100. Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSp, pp. 108–116 (2018)

  101. Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357–374 (2012)

    Article  Google Scholar 

  102. Sicari, S., Rizzardi, A., Grieco, L.A., Coen-Porisini, A.: Security, privacy and trust in Internet of Things: the road ahead. Comput. Netw. 76, 146–164 (2015)

    Article  Google Scholar 

  103. Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems, pp. 3483–3491 (2015)

  104. Susilo, B., Sari, R.F.: Intrusion detection in IoT networks using deep learning algorithm. Information 11(5), 279 (2020)

    Article  Google Scholar 

  105. Syarif, I., Prugel-Bennett, A., Wills, G.: Unsupervised clustering approach for network anomaly detection. In: International Conference on Networked Digital Technologies, pp. 135–145. Springer (2012)

  106. Tajbakhsh, A., Rahmati, M., Mirzaei, A.: Intrusion detection using fuzzy association rules. Appl. Soft Comput. 9(2), 462–469 (2009)

    Article  Google Scholar 

  107. Tama, B.A., Rhee, K.H.: Attack classification analysis of IoT network via deep learning approach. ReBICTE 3, 1–9 (2017)

    Google Scholar 

  108. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the kdd cup 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6. IEEE (2009)

  109. Taylor, A., Leblanc, S., Japkowicz, N.: Anomaly detection in automobile control network data with long short-term memory networks. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 130–139. IEEE (2016)

  110. Teyou, D., Kamdem, G., Ziazet, J.: Convolutional neural network for intrusion detection system. In: Cyber Physical Systems. arXiv preprint arXiv:1905.03168 (2019)

  111. Thamilarasu, G., Chawla, S.: Towards deep-learning-driven intrusion detection for the internet of things. Sensors 19(9), 1977 (2019)

    Article  Google Scholar 

  112. Tian, Z., Luo, C., Qiu, J., Du, X., Guizani, M.: A distributed deep learning system for web attack detection on edge devices. IEEE Trans. Ind. Inform. 16(3), 1963–1971 (2019)

    Article  Google Scholar 

  113. Tran, N.N., Sarker, R., Hu, J.: An approach for host-based intrusion detection system design using convolutional neural network. In: International Conference on Mobile Networks and Management, pp. 116–126. Springer (2017)

  114. Tuor, A., Kaplan, S., Hutchinson, B., Nichols, N., Robinson, S.: Deep learning for unsupervised insider threat detection in structured cybersecurity data streams. arXiv preprint arXiv:1710.00811 (2017)

  115. Vlajic, N., Zhou, D.: IoT as a land of opportunity for DDoS hackers. Computer 51(7), 26–34 (2018)

    Article  Google Scholar 

  116. Wang, Z.: Deep learning-based intrusion detection with adversaries. IEEE Access 6, 38367–38384 (2018)

    Article  Google Scholar 

  117. Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., Zhu, M.: HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6, 1792–1806 (2017)

    Article  Google Scholar 

  118. Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), pp. 712–717. IEEE (2017)

  119. Warzyński, A., Kołaczek, G.: Intrusion detection systems vulnerability on adversarial examplhwanges. In: 2018 Innovations in Intelligent Systems and Applications (INISTA), pp. 1–4. IEEE (2018)

  120. Wong, W.G.: Developers discuss IoT security and platforms trends (2014). https://www.electronicdesign.com/technologies/embedded-revolution/article/21800154/developers-discuss-iot-security-and-platforms-trends

  121. Yavuz, F.Y., Ünal, D., Gül, E.: Deep learning for detection of routing attacks in the internet of things. Int. J. Comput. Intell. Syst. 12(1), 39–58 (2018). https://doi.org/10.2991/ijcis.2018.25905181

    Article  Google Scholar 

  122. Yu, Y., Long, J., Cai, Z.: Network intrusion detection through stacking dilated convolutional autoencoders. Secur. Commun. Netw. (2017)

  123. Yuan, X., Li, C., Li, X.: DeepDefense: identifying DDoS attack via deep learning. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–8. IEEE (2017)

  124. Zarpelão, B.B., Miani, R.S., Kawakani, C.T., de Alvarenga, S.C.: A survey of intrusion detection in Internet of Things. J. Netw. Comput. Appl. 84, 25–37 (2017)

    Article  Google Scholar 

  125. Zeng, Y., Gu, H., Wei, W., Guo, Y.: \$ Deep-Full-Range \$: a deep learning based network encrypted traffic classification and intrusion detection framework. IEEE Access 7, 45182–45190 (2019)

    Article  Google Scholar 

  126. Zhang, A.Y., Lam, S.S.W., Ong, M.E.H., Tang, P.H., Chan, L.L.: Explainable AI: classification of MRI brain scans orders for quality improvement. In: Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 95–102 (2019)

  127. Zhang, B., Yu, Y., Li, J.: Network intrusion detection based on stacked sparse autoencoder and binary tree ensemble method. In: 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6. IEEE (2018)

  128. Zhang, C., Shang, B., Wei, P., Li, L., Liu, Y., Zheng, N.: Building explainable AI evaluation for autonomous perception. In: CVPR Workshops, pp. 20–23 (2019)

  129. Zhang, H., Yu, X., Ren, P., Luo, C., Min, G.: Deep adversarial learning in intrusion detection: A data augmentation enhanced framework. arXiv preprint arXiv:1901.07949 (2019)

  130. Zhang, Y., Li, P., Wang, X.: Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access 7, 31711–31722 (2019)

    Article  Google Scholar 

  131. Zhao, L., Su, C., Huang, H., Han, Z., Ding, S., Li, X.: Intrusion detection based on device-free localization in the era of IoT. Symmetry 11(5), 630 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, International Hellenic University, Kavala Campus, Greece

    Stefanos Tsimenidis, Thomas Lagkas & Konstantinos Rantos

Authors
  1. Stefanos Tsimenidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Lagkas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Konstantinos Rantos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Lagkas.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsimenidis, S., Lagkas, T. & Rantos, K. Deep Learning in IoT Intrusion Detection. J Netw Syst Manage 30, 8 (2022). https://doi.org/10.1007/s10922-021-09621-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10922-021-09621-9

Keywords

Search

Navigation