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A behavior-based interruption detection framework for secure internet of things-based smart industry job transactions

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Abstract

Smart industry paradigms are encouraged for automated and error-less manufacturing and job shop processing with the Industrial Internet of Things. The smart manufacturing environment employs independent machines and devices for providing cost-effective outcomes. The independent operating nature of the machines/devices is lured easily through the external hijackers that degrade the production outcome. By considering the real-time smart industrial process, this paper introduces Performance-focused Process Transaction Framework. The main motive of this work is to ensure secure job transactions in the smart industry with reduced task failures and improved production efficiency. The machines' performance is observed and measured based on the previous job completion rate and successive allocation intervals. Reward-based learning is induced in this framework for monitoring the operation state of the machine/device. The centralized controller makes use of the rewards and states for deciding the allocation without adversary interruption. In this job transaction process, the machine's adversary behavior is vital in determining production efficiency. The adverse behaving nature of the devices is detected in an early stage for reducing task failures. Further, the proposed framework's performance is verified using the metrics like true negative rate, adversary impact, processing time, and task allocation rate.

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References

  • Abuhasel KA, Khan MA (2020) A secure industrial internet of things (IIoT) framework for resource management in smart manufacturing. IEEE Access 8:117354–117364

    Article  Google Scholar 

  • Alcaraz C (ed) (2019) Security and privacy trends in the industrial internet of things, vol 708. Springer, Berlin

    Google Scholar 

  • Ali W, Din IU, Almogren A, Guizani M, Zuair M (2020) A lightweight privacy-aware iot-based metering scheme for smart industrial ecosystems. IEEE Trans Ind Inf 17(9):6134–6143

    Article  Google Scholar 

  • Arachchige PCM, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M (2020) A trustworthy privacy preserving framework for machine learning in industrial IoT systems. IEEE Trans Ind Inf 16(9):6092–6102

    Article  Google Scholar 

  • Franco J, Aris A, Canberk B, Uluagac AS (2021) A survey of honeypots and honeynets for internet of things, industrial internet of things, and cyber-physical systems. IEEE Commun Surv Tutor 23(4):2351–2383

    Article  Google Scholar 

  • Hassan MM, Huda S, Sharmeen S, Abawajy J, Fortino G (2020) An adaptive trust boundary protection for IIoT networks using deep-learning feature-extraction-based semisupervised model. IEEE Trans Ind Inf 17(4):2860–2870

    Article  Google Scholar 

  • Khan LU, Alsenwi M, Yaqoob I, Imran M, Han Z, Hong CS (2020) Resource optimized federated learning-enabled cognitive internet of things for smart industries. IEEE Access 8:168854–168864

    Article  Google Scholar 

  • Khoda ME, Imam T, Kamruzzaman J, Gondal I, Rahman A (2019) Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples. IEEE Trans Ind Appl 56(4):4415–4424

    Google Scholar 

  • Kong L, Liu XY, Sheng H, Zeng P, Chen G (2019) Federated tensor mining for secure industrial internet of things. IEEE Trans Ind Inf 16(3):2144–2153

    Article  Google Scholar 

  • Li J, Dai J, Issakhov A, Almojil SF, Souri A (2021) Towards decision support systems for energy management in the smart industry and Internet of Things. Comput Ind Eng 161:107671

    Article  Google Scholar 

  • Liang F, Yu W, Liu X, Griffith D, Golmie N (2020) Toward edge-based deep learning in industrial Internet of Things. IEEE Internet Things J 7(5):4329–4341

    Article  Google Scholar 

  • Lin H, Hu J, Wang X, Alhamid MF, Piran MJ (2020) Toward secure data fusion in industrial IoT using transfer learning. IEEE Trans Ind Inf 17(10):7114–7122

    Article  Google Scholar 

  • Liu CH, Lin Q, Wen S (2018) Blockchain-enabled data collection and sharing for industrial IoT with deep reinforcement learning. IEEE Trans Ind Inf 15(6):3516–3526

    Article  Google Scholar 

  • Liu M, Qu N, Tang J, Chen Y, Song H, Gong F (2020) Signal estimation in cognitive satellite networks for satellite-based industrial internet of things. IEEE Trans Ind Inf 17(3):2062–2071

    Google Scholar 

  • Lv Z, Han Y, Singh AK, Manogaran G, Lv H (2020) Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Trans Ind Inf 17(2):1496–1504

    Article  Google Scholar 

  • Mukherjee A, Goswami P, Yang L, Sah Tyagi SK, Samal UC, Mohapatra SK (2020) Deep neural network-based clustering technique for secure IIoT. Neural Comput Appl 32:16109–16117

    Article  Google Scholar 

  • Ren Y, Zhu F, Qi J, Wang J, Sangaiah AK (2019) Identity management and access control based on blockchain under edge computing for the industrial internet of things. Appl Sci 9(10):2058

    Article  Google Scholar 

  • Rouzbahani HM, Bahrami AH, Karimipour H (2021) A snapshot ensemble deep neural network model for attack detection in industrial internet of things. AI-Enabled Threat Detection and Security Analysis for Industrial IoT, 181–194

  • Salonikias S, Gouglidis A, Mavridis I, Gritzalis D (2019) Access control in the industrial internet of things. Security and privacy trends in the industrial internet of things, 95–114

  • Shahzad Y, Javed H, Farman H, Ahmad J, Jan B, Zubair M (2020) Internet of energy: opportunities, applications, architectures and challenges in smart industries. Comput Electr Eng 86:106739

    Article  Google Scholar 

  • Singh SK, Jeong YS, Park JH (2020) A deep learning-based IoT-oriented infrastructure for secure smart city. Sustain Cities Soc 60:102252

    Article  Google Scholar 

  • Singh P, Masud M, Hossain MS, Kaur A (2021) Cross-domain secure data sharing using blockchain for industrial IoT. J Parallel Distrib Comput 156:176–184

    Article  Google Scholar 

  • Sisi Z, Souri A (2021) Blockchain technology for energy‐aware mobile crowd sensing approaches in Internet of Things. Transactions on Emerging Telecommunications Technologies, 4217

  • Umran SM, Lu S, Abduljabbar ZA, Zhu J, Wu J (2021) Secure data of industrial internet of things in a cement factory based on a Blockchain technology. Appl Sci 11(14):6376

    Article  Google Scholar 

  • Wu W, Yang P, Zhang W, Zhou C, Shen X (2020) Accuracy-guaranteed collaborative DNN inference in industrial IoT via deep reinforcement learning. IEEE Trans Ind Inf 17(7):4988–4998

    Article  Google Scholar 

  • Zhang X, Chen X, Liu JK, Xiang Y (2019) DeepPAR and DeepDPA: privacy preserving and asynchronous deep learning for industrial IoT. IEEE Trans Ind Inf 16(3):2081–2090

    Article  Google Scholar 

  • Zhang G, Chen CH, Zheng P, Zhong RY (2020) An integrated framework for active discovery and optimal allocation of smart manufacturing services. J Clean Prod 273:123144

    Article  Google Scholar 

  • Zhang W, Yang D, Peng H, Wu W, Quan W, Zhang H, Shen X (2021) Deep reinforcement learning based resource management for DNN inference in industrial IoT. IEEE Trans Veh Technol 70(8):7605–7618

    Article  Google Scholar 

  • Ziegler D, Sabongui J, Palfinger G (2019) Fine-grained access control in industrial Internet of Things: evaluating outsourced attribute-based encryption. In: ICT systems security and privacy protection: 34th IFIP TC 11 international conference, SEC 2019, Lisbon, Portugal, June 25–27, 2019, Proceedings 34. Springer International Publishing. pp 91–104

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, 603203, India

    M. Vijayakumar

  2. Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, Tamil Nadu, 603203, India

    T. S. Shiny Angel

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  1. M. Vijayakumar
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  2. T. S. Shiny Angel
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Correspondence to M. Vijayakumar.

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Vijayakumar, M., Shiny Angel, T.S. A behavior-based interruption detection framework for secure internet of things-based smart industry job transactions. Soft Comput 27, 11801–11813 (2023). https://doi.org/10.1007/s00500-023-08767-9

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