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Systematic Study of Detection Mechanism for Network Intrusion in Cloud, Fog, and Internet of Things Using Deep Learning

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Human-Centric Smart Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 316))

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Abstract

Development of latest technologies creates human life more convenient and easier. However, along with such technological advancements, several complications are generated in various segments. Network security also experiences inconvenient situations those are literally originated from infinite number of complex intrusions. A network intrusion detection system (NIDS) is an advanced and revolutionary system that has been established to resolve the problematic behaviors of the networking environment through accurate detection of unidentified attacks. Several methods and techniques have been taken active part for the development of an ideal NIDS but merging with deep learning technologies, NIDS achieves miracle performance against various intrusive activities in the security domain. In this paper, we serialize and present an adequate number of existing deep learning-based NIDSs in the Internet of things (IoT), cloud, fog, and edge networks domain. Different NIDS approaches along with their utilization, advantages, and restrictions are perfectly described in this paper so that people can achieve proper and detailed knowledge of security issues in the above-mentioned networks.

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References

  1. Zhang, H., Wu, C. Q., Gao, S., Wang, Z., Xu, Y., Liu, Y.: An effective deep learning based scheme for network intrusion detection. In: 24th International Conference on Pattern Recognition (ICPR), pp. 682–687. IEEE (2018)

    Google Scholar 

  2. Al-Emadi, S., Al-Mohannadi, A., Al-Senaid, F.: Using deep learning techniques for network intrusion detection.In: IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pp. 171–176. IEEE (2020)

    Google Scholar 

  3. Lakshminarayana, D.H., Philips, J., Tabrizi, N.: A survey of intrusion detection techniques. In: IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1122–1129. IEEE (2019)

    Google Scholar 

  4. Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P. L., Iorkyase, E., Tachtatzis, C.,Atkinson, R.: Threat analysis of IoT networks using artificial neural network intrusion detection system. In: International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2016)

    Google Scholar 

  5. Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G.,Robles-Kelly, A.: Deep learning-based intrusion detection for IoT networks. In: IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 256–25609. IEEE (2019)

    Google Scholar 

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

    Google Scholar 

  7. RM, S.P., Maddikunta, P.K.R., Parimala, M., Koppu, S., Gadekallu, T.R., Chowdhary, C.L., Alazab, M.: An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput. Commun. 160, 139–149. Elsevier (2020)

    Google Scholar 

  8. Sriram, S., Vinayakumar, R., Alazab, M.,Soman, K. P.: Network flow based IoT botnet attack detection using deep learning. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 189–194. IEEE (2020)

    Google Scholar 

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

    Google Scholar 

  10. Balakrishnan, N., Rajendran, A., Pelusi, D.,Ponnusamy, V.: Deep Belief Network enhanced intrusion detection system to prevent security breach in the internet of things. Int Things 14, p. 100112. Elsevier (2019)

    Google Scholar 

  11. Li, Y., Xu, Y., Liu, Z., Hou, H., Zheng, Y., Xin, Y., Zhao, Y., Cui, L.: Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement 154, p. 107450. Elsevier (2020)

    Google Scholar 

  12. Van Huong, P., Hung, D.V.: Intrusion detection in IoT systems based on deep learning using convolutional neural network. In: 6th NAFOSTED Conference on Information and Computer Science (NICS), pp. 448–453. IEEE (2019)

    Google Scholar 

  13. Rezvy, S., Luo, Y., Petridis, M., Lasebae, A., Zebin, T.: An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks.In: 53rd Annual Conference on information sciences and systems (CISS), pp. 1–6. IEEE (2019)

    Google Scholar 

  14. Roopak, M., Tian, G. Y.,Chambers, J.: An intrusion detection system against ddos attacks in iot networks.In: 10th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0562–0567. IEEE (2020)

    Google Scholar 

  15. Hajimirzaei, B., Navimipour, N.J.: Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. In: ICT Express, vol. 5, issue. 1, pp. 56–59. Elsevier (2019)

    Google Scholar 

  16. Parampottupadam, S., Moldovann, A.N.: Cloud-based real-time network intrusion detection using deep learning. In: International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp. 1–8. IEEE (2018)

    Google Scholar 

  17. Kanimozhi, V.,Jacob, T. P.: Artificial intelligence based network intrusion detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing. In: International conference on communication and signal processing (ICCSP), pp. 0033–0036. IEEE (2019)

    Google Scholar 

  18. Chiba, Z., Abghour, N., Moussaid, K.,Rida, M.: A cooperative and hybrid network intrusion detection framework in cloud computing based on snort and optimized back propagation neural network. Proc. Comput. Sci. 83, pp. 1200–1206. Elsevier (2016)

    Google Scholar 

  19. Abusitta, A., Bellaiche, M., Dagenais, M.,Halabi, T.: A deep learning approach for proactive multi-cloud cooperative intrusion detection system. Future Generation Comput. Syst. 98, pp. 308–318. Elsevier (2019)

    Google Scholar 

  20. Mayuranathan, M., Murugan, M., Dhanakoti, V.: Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment. J. Ambient Intell. Hum. Comput. 12(3), pp. 3609–3619. Springer (2021)

    Google Scholar 

  21. Nguyen, K.K., Hoang, D.T., Niyato, D., Wang, P., Nguyen, D., Dutkiewicz, E.: Cyberattack detection in mobile cloud computing: a deep learning approach. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2018)

    Google Scholar 

  22. Thakkar, N., Karamta, M., Joshi, S., Potdar, M. B.: Anomaly detection and categorization in cloud environment using deep learning Techniques. Int. J. Comput. Sci. Eng. (IJCSE) 7(5), pp. 211–214 (2019)

    Google Scholar 

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

    Google Scholar 

  24. Samriya, J. K., Kumar, N.: A novel intrusion detection system using hybrid clustering-optimization approach in cloudcomputing. Mater. Today Proc. Elsevier (2020)

    Google Scholar 

  25. Pandeeswari, N., Kumar, G.: Anomaly detection system in cloud environment using fuzzy clustering based ANN. Mob. Netw. Appli. 21(3), pp. 494–505. Springer (2016)

    Google Scholar 

  26. Pacheco, J., Benitez, V.H., Felix-Herran, L.C., Satam, P.: Artificial neural networks-based intrusion detection system for internet of things fog nodes. IEEE Access 8, pp. 73907–73918. IEEE (2020)

    Google Scholar 

  27. Abeshu, A., Chilamkurti, N.: Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Commun. Mag. 56(2), pp. 169–175. IEEE (2018)

    Google Scholar 

  28. NG, B.A., Selvakumar, S.: Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment. In: Future Generation Computer Systems, vol. 113, pp. 255–265. Elsevier (2020)

    Google Scholar 

  29. Sadaf, K., Sultana, J.: Intrusion detection based on auto encoder and isolation forest in fog computing. IEEE Access 8, pp. 167059–167068. IEEE (2020)

    Google Scholar 

  30. Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., Razaque, A.: Deep recurrent neural network for IoT intrusion detection system. Simul. Model. Pract. Theor. 101, p. 102031. Elsevier (2020)

    Google Scholar 

  31. Diro, A., Chilamkurti, N.: Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun. Mag. 56(9), pp. 124–130 (2018)

    Google Scholar 

  32. Priyadarshini, R., Barik, R.K.: A deep learning based intelligent framework to mitigate DDoS attack in fog environment. J. King Saud Univ. Comput. Inf. Sci. Elsevier (2019)

    Google Scholar 

  33. de Souza, C.A., Westphall, C.B., Machado, R.B., Sobral, J.B.M., dos Santos Vieira, G.: Hybrid approach to intrusion detection in fog-based IoT environments. In: Computer Networks, vol. 180, p. 107417. Elsevier (2020)

    Google Scholar 

  34. Abdel-Basset, M., Chang, V., Hawash, H., Chakrabortty, R.K., Ryan, M.: Deep-IFS: intrusion detection approach for IIoT traffic in fog environment. In: IEEE Transactions on Industrial Informatics. IEEE (2020)

    Google Scholar 

  35. Tian, Z., Luo, C., Qiu, J., Du, X., Guizani, M.: A distributed deep learning system for web attack detection on edge devices. In: IEEE Transactions on Industrial Informatics, vol. 16, 3, pp. 1963–1971. IEEE (2019)

    Google Scholar 

  36. Almogren, A. S.: Intrusion detection in edge-of-things computing. J. Parallel Distrib. Comput 137, pp. 259–265. Elsevier (2020)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Mizoram University, Aizawal, Mizoram, 796004, India

    Sanjukta Bhattacharya & Ajoy Kumar Khan

  2. Department of Information Technology, Techno International Newtown, Newtown, West Bengal, 700156, India

    Sanjukta Bhattacharya

  3. Department of Applied Electronics and Instrumentation Engineering, Heritage Institute of Technology, Anandapur, Kolkata, West Bengal, 700107, India

    Santanu Ghorai

Authors
  1. Sanjukta Bhattacharya
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  2. Santanu Ghorai
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  3. Ajoy Kumar Khan
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Correspondence to Sanjukta Bhattacharya .

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Editors and Affiliations

  1. Rajnagar Mahavidyalaya, Rajnagar, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering (AI and ML), Bengal Institute of Technology, Kolkata, India

    Jyoti Sekhar Banerjee

  3. Department of Computer Science and Electronics, Kyushu Institute of Technology, Fukuoka, Japan

    Mario Köppen

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Bhattacharya, S., Ghorai, S., Khan, A.K. (2023). Systematic Study of Detection Mechanism for Network Intrusion in Cloud, Fog, and Internet of Things Using Deep Learning. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_3

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