FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-100332

Research Article |

A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation

Author(s): Bibhuti Bhusana Behera1, Rajani Kanta Mohanty2 and Binod Kumar Pattanayak3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 3

Publisher : FOREX Publication

Published : 15 September 2022

e-ISSN : 2347-470X

Page(s) : 604-613

DOI: https://doi.org/10.37391/IJEER.100332

Abstract

Industrial Internet of Things (IIoT) is a new field of study that connects digital devices and services to physical systems. The IIoT has been utilized to create massive amounts of data from various sensors, and it has run into several problems. The IIoT has been subjected to a variety of hacks, putting its ability to provide enterprises with flawless operations in jeopardy. Businesses suffer financial and reputational losses as a result of such threats, as well as the theft of critical data. As a result, numerous Network Intrusion Detection Systems (NIDSs) have been created to combat and safeguard IIoT systems, but gathering data that can be utilized in the construction of an intelligent NIDS is a tough operation; consequently, identifying current and new assaults poses major issues. In this research work, a novel IIOT attack detection framework and mitigation model is designed by following four major phases “(a) pre-processing, (b) feature extraction, (c) feature selection and (d) attack detection”. Initially, the collected raw data (input) is subjected to pre-processing phase, wherein the data cleaning and data standardization operations take place. Subsequently, the features like “higher-order statistical features (Skewness, Kurtosis, Variance and Moments), technical indicator based features, mutual information, Improved Principal Component Analysis (IPCA)” based features are extracted from the pre-processed data. Further, from the extracted features, the most optimal features are selected using a new hybrid optimization model referred as Hunger Customized Individual Activity Model (HCIA) that hybrids the concepts of standard (Teamwork Optimization Algorithm (TOA) and Hunger Games Search (HGS)). The attack detection is carried out using the projected deep fusion model framework that encapsulates the Bi-GRU and Quantum Deep Neural Network (QDNN), respectively. The Bi-GRU and QDNN in the deep fusion model framework is trained with the optimal features selected using a new hybrid optimization model. The outcome acquired from Bi-GRU and QDNN is combined, and it will be the final detected outcome that portrays the presence/ absence of attacks in IIoT network. When an attack is being identified, the mitigation of such attack takes place via the Improved BIAT Framework. Further, the projected model is evaluated over the existing models to show its supremacy in the attack detection and mitigation process.

Keywords: IIoT, Attack Detection, Deep Learning Model, Improved BAIT, HCIA




Bibhuti Bhusana Behera, Department of Computer Science and Engineering. Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India; Email: bibhuti.1973@gmail.com

Rajani Kanta Mohanty, Department of Computer Science and Engineering-SP, Jain University, Bengaluru, India; Email: rkm.bbs@gmail.com

Binod Kumar Pattanayak, Department of Computer Science and Engineering. Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India; Email: binodpattanayak@soa.ac.in

    [1] Z Sharmistha Nayaka, Nurzaman Ahmed, Sudip Misra, "Deep Learning-Based Reliable Routing Attack Detection Mechanism for Industrial Internet of Things", Ad Hoc Networks, VOl.123,2021[Cross Ref]
    [2] Sumaiya Thaseen Ikram, V. Priya, B. Anbarasu, Xiaochun Cheng, Muhammad Rukunuddin Ghalib & Achyut Shankar, "Prediction of IIoT traffic using a modified whale optimization approach integrated with random forest classifier", The Journal of Supercomputing , 2022[Cross Ref]
    [3] Xihua, Z. and Goyal, S.B., “Security and Privacy Challenges using IoT-Blockchain Technology in a Smart City: Critical Analysis”, IJEER, Survey Report, Volume 10, Issue 2, Pages 190-195.[Cross Ref]
    [4] Lupeng Zhang,Pingchuan Wang,Fengqi Li,"An adaptive and robust secret key extraction scheme from high noise wireless channel in IIoT",Digital Communications and Networks, 2022[Cross Ref]
    [5] Kumar, S., Yadav, R., Kaushik, P., Babu, S.T., Dubey, R.K. and Subramanian, M., Effective Cyber Security Using IoT to Prevent E-Threats and Hacking During Covid-19. IJEER, Research Article, Volume 10, Issue 2, Pages 111-116.[Cross Ref]
    [6] Yi Sun,Ali Kashif Bashir,Fei Xiao,"Effective malware detection scheme based on classified behavior graph in IIoT", Ad Hoc Networks, 2021[Cross Ref]
    [7] S. M. Kasongo, "An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms," in IEEE Access, vol. 9, pp. 113199-113212, 2021. doi: 10.1109/ACCESS.2021.3104113[Cross Ref]
    [8] H. Cho, S. Lim, V. Belenko, M. Kalinin, D. Zegzhda and E. Nuralieva, "Application and improvement of sequence alignment algorithms for intrusion detection in the Internet of Things," 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 2020, pp. 93-97. doi: 10.1109/ICPS48405.2020.9274752[Cross Ref]
    [9] J. Long, F. Fang and H. Luo, "A Survey of Machine Learning-based IoT Intrusion Detection Techniques," 2021 IEEE 6th International Conference on Smart Cloud (SmartCloud), Newark, NJ, USA, 2021, pp. 7-12. doi: 10.1109/SmartCloud52277.2021.00009[Cross Ref]
    [10] Kavitha, A., Rao, B.S., Akhtar, N., Rafi, S.M., Singh, P., Das, S. and Manikandan, G., A Novel Algorithm to Secure Data in New Generation Health Care System from Cyber Attacks Using IoT. IJEER, Research Article, Volume 10, Issue 2, Pages 270-275 [Cross Ref]
    [11] H. Lu, T. Wang, X. Xu and T. Wang, "Cognitive Memory-Guided AutoEncoder for Effective Intrusion Detection in Internet of Things," in IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3358-3366, May 2022. doi: 10.1109/TII.2021.3102637[Cross Ref]
    [12] B. Naik, M. S. Obaidat, J. Nayak, D. Pelusi, P. Vijayakumar and S. H. Islam, "Intelligent Secure Ecosystem Based on Metaheuristic and Functional Link Neural Network for Edge of Things," in IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1947-1956, March 2020. doi: 10.1109/TII.2019.2920831[Cross Ref]
    [13] F. Farivar, M. S. Haghighi, A. Jolfaei and M. Alazab, "Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT," in IEEE Transactions on Industrial Informatics, vol. 16, no. 4, pp. 2716-2725, April 2020. doi: 10.1109/TII.2019.2956474[Cross Ref]
    [14] A. A. Kurniawan, H. A. Santoso, M. A. Soeleman and A. Z. Fanani, "Intrusion Detection System as Audit in IoT Infrastructure using Ensemble Learning and SMOTE Method," 2019 5th International Conference on Science in Information Technology (ICSITech), Yogyakarta, Indonesia, 2019, pp. 205-210. doi: 10.1109/ICSITech46713.2019.8987524[Cross Ref]
    [15] P. V. Huong, L. D. Thuan, L. T. Hong Van and D. V. Hung, "Intrusion Detection in IoT Systems Based on Deep Learning Using Convolutional Neural Network," 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam, 2019, pp. 448-453. doi: 10.1109/NICS48868.2019.9023871[Cross Ref]
    [16] S. H. S. Ariffin, C. J. Le and N. H. A. Wahab, "Configuring Local Rule of Intrusion Detection System in Software Defined IoT Testbed," 2021 26th IEEE Asia-Pacific Conference on Communications (APCC), Kuala Lumpur, Malaysia, 2021, pp. 298-303. doi: 10.1109/APCC49754.2021.9609824[Cross Ref]
    [17] G. Abdelmoumin, D. B. Rawat and A. Rahman, "On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things," in IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4280-4290, 15 March15, 2022. doi: 10.1109/JIOT.2021.3103829[Cross Ref]
    [18] N. Sehatbakhsh et al., "REMOTE: Robust External Malware Detection Framework by Using Electromagnetic Signals," in IEEE Transactions on Computers, vol. 69, no. 3, pp. 312-326, 1 March 2020. doi: 10.1109/TC.2019.2945767[Cross Ref]
    [19] M. M. Moussa and L. Alazzawi, "Cyber Attacks Detection based on Deep Learning for Cloud-Dew Computing in Automotive IoT Applications," 2020 IEEE International Conference on Smart Cloud (SmartCloud), Washington, DC, USA, 2020, pp. 55-61. doi: 10.1109/SmartCloud49737.2020.00019[Cross Ref]
    [20] Mohammad Dehghani and Pavel Trojovský,"Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization", Sensors, 2021[Cross Ref]
    [21] Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H, "Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts", Expert Systems with Applications, Vol.155, 2021[Cross Ref]
    [22] Malige Gangappa,Kiran Mai C,Sammulal P, "Enhanced Crow Search Optimization Algorithm and Hybrid NN-CNN Classifiers for Classification of Land Cover Images", Multimedia Research, Vol.2,No.3, pp.12-22,2019.[Cross Ref]
    [23] kulkarni,Senthil Murugan T, "Hybrid Weed-Particle Swarm Optimization Algorithm and C- Mixture for Data Publishing", Multimedia Research, Vol.2,No.3, pp.33-42,2019."[Cross Ref]
    [24] Santosh Kumar B. P,Venkata Ramanaiah K., "An Efficient Hybrid Optimization Algorithm for Image Compression", Multimedia Research, Vol.2,No.4, pp.1-11,2019.[Cross Ref]
    [25] Addanki Kavitha, B Srinivasa Rao, Dr Nikhat Akhtar, Dr Shaik Mohammad Rafi, Prabhdeep Singh, Dr Sunanda Das and Dr G Manikandan (2022), A Novel Algorithm to Secure Data in New Generation Health Care System from Cyber Attacks Using IoT. IJEER 10(2), 270-275. DOI: 10.37391/IJEER.100236.[Cross Ref]

Bibhuti Bhusana Behera, Rajani Kanta Mohanty, Binod Kumar Pattanayak (2022), A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation. IJEER 10(3), 604-613. DOI: 10.37391/IJEER.100332.