Abstract
The Industrial Internet of Things (IIoT) plays an important role as its implementation requires scalable and secure methods. IIoT integrates databases, machines, various sensors, and services to improve our lives in many ways, including e-healthcare and smarter cities. Accurately identifying attacks in IIoT is critical to mitigating security risks, and the volume of such attacks is rising to an unprecedented level. As IIoT solutions integrate into larger operational systems, additional planning and development of a new mechanism is required to ensure privacy and security. Many deep learning approaches have been developed to focus on intrusion detection. However, such approaches suffer from low detection accuracy, efficiency, privacy, and high time consumption. To mitigate these issues, this study proposes a novel approach called bi-long short-term memory (Bi-LSTM) in IIoT security for privacy and security in industrial systems. To improve the performance of Bi-LSTM, we use modified coot optimization. Our proposed Bi-LSTM approach effectively detects attacks in the IIoT environment. To assess its performance, we analyze various evaluation measures including precision, sensitivity, Matthews correlation coefficient (MCC), and accuracy. The results of our performance analysis reveal that the proposed optimized Bi-LSTM method achieves a higher performance rate of 98.91% accuracy, 95.86% sensitivity, 95.63% MCC, and 97.52% accuracy than other existing approaches. The existing approaches such as CDW_FedAvg, RNN, COSNN, and RCS-DOFH achieved lower accuracy of 98.52%, 97.83%, 95.17%, and 96.24%, respectively. The findings highlight the potential for improved cybersecurity in the IIoT, emphasizing the importance of effective planning and innovative critical systems.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Rahman, A., Islam, M.J., Band, S.S., Muhammad, G., Hasan, K., Tiwari, P.: Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT. Digit. Commun. Netw. 9(2), 411–421 (2023)
Mrabet, H., Alhomoud, A., Jemai, A., Trentesaux, D.: A secured industrial internet-of-things architecture based on Blockchain technology and machine learning for sensor access control systems in smart manufacturing. Appl. Sci. 12(9), 4641 (2022)
Kumar, P., Kumar, R., Abhinav Kumar, A., Franklin, A., Garg, S., Singh, S.: Blockchain and deep learning for secure communication in digital twin empowered industrial IoT network. IEEE Trans. Netw. Sci. Eng. 10(5), 2802–2813 (2023). https://doi.org/10.1109/TNSE.2022.3191601
Fu, J.S., Liu, Y., Chao, H.C., Bhargava, B.K., Zhang, Z.J.: Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing. IEEE Trans. Industr. Inf. 14(10), 4519–4528 (2018)
Rathore, S., Kwon, B.W., Park, J.H.: BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network. J. Netw. Comput. Appl. 143, 167–177 (2019)
Sengupta, J., Ruj, S., Bit, S.D.: A secure fog-based architecture for industrial internet of things and industry 4.0. IEEE Trans. Ind. Inf. 17(4), 2316–2324 (2020)
Portal, G., de Matos, E., Hessel, F.: An edge decentralized security architecture for industrial iot applications. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1–6 (2020)
Umran, S.M., Lu, S., Abduljabbar, Z.A., Zhu, J., Wu, J.: Secure data of industrial internet of things in a cement factory based on a Blockchain technology. Appl. Sci. 11(14), 6376 (2021)
Pal, S., Jadidi, Z.: Analysis of security issues and countermeasures for the industrial internet of things. Appl. Sci. 11(20), 9393 (2021)
Kiran, K.S., Devisetty, R.K., Kalyan, N.P., Mukundini, K., Karthi, R.: Building a intrusion detection system for IoT environment using machine learning techniques. Proc. Comput. Sci. 171, 2372–2379 (2020)
Zhang, W., Lu, Q., Yu, Q., Li, Z., Liu, Y., Lo, S.K., Chen, S., Xu, X., Zhu, L.: Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet Things J. 8(7), 5926–5937 (2020)
Senthilkumar, P., Rajesh, K.: Design of a model based engineering deep learning scheduler in cloud computing environment using industrial internet of things (IIOT). J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-020-02862-7
Li, Q., Yue, Y., Wang, Z.: Deep robust cramershoup delay optimized fully homomorphic for IIOT secured transmission in cloud computing. Comput. Commun. 161, 10–18 (2020)
Selvarajan, S., Srivastava, G., Khadidos, A.O., Khadidos, A.O., Baza, M., Alshehri, A., Lin, J.C.W.: An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems. J. Cloud Comput. 12(1), 38 (2023)
Bello, I., Chiroma, H., Abdullahi, U.A., Gital, A.Y.U., Jauro, F., Khan, A., Okesola, J.O., Abdulhamid, S.I.M.: Detecting ransomware attacks using intelligent algorithms: Recent development and next direction from deep learning and big data perspectives. J. Ambient. Intell. Humaniz. Comput. 12, 8699–8717 (2021)
Prabakar, D., Sundarrajan, M., Manikandan, R., Jhanjhi, N.Z., Masud, M., Alqhatani, A.: Energy analysis-based cyber attack detection by IoT with artificial intelligence in a sustainable smart city. Sustainability 15(7), 6031 (2023)
Ashraf, H., Khan, F., Ihsan, U., Al-Quayed, F., Jhanjhi, N.Z., Humayun, M.: MABPD: Mobile Agent-Based Prevention and Black Hole Attack Detection in Wireless Sensor Networks. In 2023 International Conference on Business Analytics for Technology and Security (ICBATS), pp. 1–11 (2023)
Bhattacharya, P., Tanwar, S., Bodkhe, U., Tyagi, S., Kumar, N.: Bindaas: Blockchain-based deep-learning as-a-service in healthcare 4.0 applications. IEEE Trans. Netw. Sci. Eng. 8(2), 1242–1255 (2019)
Afaq, Y., Manocha, A.: Blockchain and deep learning integration for various application: a review. J. Comput. Inf. Syst. (2023). https://doi.org/10.1080/08874417.2023.2173330
Siniosoglou, I., Xouveroudis, K., Argyriou, V., Lagkas, T., Goudos, S.K., Psannis, K.E., Sarigiannidis, P.: Evaluating the effect of volatile federated timeseries on modern DNNs: attention over long/short memory. In 2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp. 1–6 (2023).
Aljuhani, A., Kumar, P., Alanazi, R., Albalawi, T., Okba Taouali, A.K.M., Islam, N., Kumar, N., Alazab, M.: A deep-learning-integrated blockchain framework for securing industrial IoT. IEEE Internet of Things J. 11(5), 7817–7827 (2024). https://doi.org/10.1109/JIOT.2023.3316669
Guha Roy, D., Srirama, S.N.: A blockchain-based cyber attack detection scheme for decentralized internet of things using software-defined network. Softw.: Pract. Exp. 51(7), 1540–1556 (2021)
Ajayi, O., Cherian, M., Saadawi, T.: Secured cyber-attack signatures distribution using blockchain technology. In 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pp. 482–488 (2019).
Kelli, V., Argyriou, V., Lagkas, T., Fragulis, G., Grigoriou, E., Sarigiannidis, P.: IDS for industrial applications: a federated learning approach with active personalization. Sensors 21(20), 6743 (2021)
Ajayi, O., Abouali, M., Saadawi, T.: Secure architecture for inter-healthcare electronic health records exchange. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–6 (2020).
Albakri, A., Alabdullah, B., Alhayan, F.: Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model. Sustainability 15(18), 13887 (2023)
Li, Q., Zhao, J.: An Intrusion Detection Method for CBTC Systems Using Blockchain and LSTM. In 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), pp. 609–612 (2023).
Naruei, I., Keynia, F.: A new optimization method based on COOT bird natural life model. Expert Syst. Appl. 183, 115352 (2021)
Scanning Ferrag, M.A., Friha, O., Hamouda, D., Maglaras, L., Janicke, H.: Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access 10, 40281–40306 (2022)
Kshirsagar, D., Kumar, S.: An efficient feature reduction method for the detection of DoS attack. ICT Express 7(3), 371–375 (2021)
Injection Tran, N.N., Pota, H.R., Tran, Q.N., Yin, X., Hu, J.: Designing false data injection attacks penetrating AC-based bad data detection system and FDI dataset generation. Concurr. Comput. : Pract. Exp. 34(7), e5956 (2022)
Sebbar, A., Zkik, K., Baddi, Y., Boulmalf, M., Kettani, M.D.E.C.E.: MitM detection and defense mechanism CBNA-RF based on machine learning for large-scale SDN context. J. Ambient Intell. Humaniz. Comput. 11(12), 5875–5894 (2020)
Zhang, X., Zhou, Y., Pei, S., Zhuge, J., Chen, J.: Adversarial examples detection for XSS attacks based on generative adversarial networks. IEEE Access 8, 10989–10996 (2020)
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
All authors agreed on the content of the study. MOA, SHH, SB, and HSHH collected all the data for analysis. HSHH agreed on the methodology and completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Human and animal rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Alassafi, M.O., Hasan, S.H., Badri, S. et al. Optimized Bi-LSTM: a novel approach for attack detection in industrial IoT. SIViP (2024). https://doi.org/10.1007/s11760-024-03125-0
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11760-024-03125-0