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Cyber Forensic Investigation in IoT Using Deep Learning Based Feature Fusion in Big Data

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Abstract

The Internet of Things (IoT) device is becoming universal domain and its success cannot be ignored, but its threats on IoT devices increases concurrently. The Cyber-attacks are becoming the component of IoT affecting user’s life. The professionals are forced to sift huge data to unveil and manage litigations. Hence, secure IoT is required for comprehending attacks. A model is presented for discovering cyber attack considering feature fusion. The routing of data towards Base Station (BS) is done with the Fractional gravitational search algorithm (FGSA). At BS, cybercrime detection is done, wherein data is splitted with enhanced Fuzzy c-means clustering (FCM) considering the MapReduce model. In mapper, the feature fusion is done with mutual information and the Deep Quantum Neural Network (DQNN), while reducer performs cybercrime detection. The Fractional Mayfly Shepherd Optimization (FrMSO)-based Deep Belief Network (DBN) is devised for describing the digital examination to notice and trace behaviors of attacks in IoT. Here, the training of DBN is done by the proposed FrMSO algorithm, which is developed by integrating the Fractional Calculus (FC), Mayfly Optimization Algorithm (MA), and the Shuffled shepherd optimization Algorithm (SSOA). The developed model helps to employ the weights of DBN with FrMSO for determining and tracing the abnormal aspects in IoT. The FrMSO-based DBN presented elevated precision of 96.4%, recall of 98.3% and F-measure of 95.4% respectively.

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Data Availability

UCSD Network Telescope Aggregrated DDoS Metadata, “https://catalog.caida.org/details/dataset/telescope_ddos”, accessed on January 2022.

References

  1. P. Y. Chen, S. M. Cheng and K. C. Chen, Information fusion to defend intentional attack in internet of things, IEEE Internet of Things Journal, Vol. 1, No. 4, pp. 337–348, 2014.

    Article  MathSciNet  Google Scholar 

  2. E. F. Jesus, V. R. Chicarino, C. V. De Albuquerque and A. A. D. A. Rocha, A survey of how to use blockchain to secure internet of things and the stalker attack, Security and Communication Networks, 2018. https://doi.org/10.1155/2018/9675050.

    Article  Google Scholar 

  3. A. A. Diro and N. Chilamkurti, Distributed attack detection scheme using deep learning approach for internet of things, Future Generation Computer Systems, Vol. 82, pp. 761–768, 2018.

    Article  Google Scholar 

  4. K. Mohammed, A.H., Jebamikyous, H., Nawara, D. and Kashef, R, 2021 Iot cyber-attack detection: A comparative analysis, In International Conference on Data Science, E-learning and Information Systems, pp. 117–123

  5. Y. N. Soe, Y. Feng, P. I. Santosa, R. Hartanto and K. Sakurai, Machine learning-based IoT-botnet attack detection with sequential architecture, Sensors, Vol. 20, No. 16, pp. 4372, 2020.

    Article  Google Scholar 

  6. Q. Abu Al-Haija and S. Zein-Sabatto, An efficient deep-learning-based detection and classification system for cyber-attacks in IoT communication networks, Electronics, Vol. 9, No. 12, pp. 2152, 2020.

    Article  Google Scholar 

  7. M. Saharkhizan, A. Azmoodeh, A. Dehghantanha, K. K. R. Choo and R. M. Parizi, An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic, IEEE Internet of Things Journal, Vol. 7, No. 9, pp. 8852–8859, 2020.

    Article  Google Scholar 

  8. Cervantes, C.; Poplade, D.; Nogueira, M.; Santos, A, Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things, In Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015

  9. Guo, Z.; Harris, I.G.; Jiang, Y.; Tsaur, L.F, An efficient approach to prevent battery exhaustion attack on BLE-based mesh networks, In Proceedings of the International Conference on Computing, Networking and Communications (ICNC), 2017

  10. B. Jia, Y. Ma, X. Huang, Z. Lin and Y. Sun, A novel real-time DDoS attack detection mechanism based on MDRA algorithm in big data, Math. Probl. Eng., 2016. https://doi.org/10.1155/2016/1467051.

    Article  MathSciNet  MATH  Google Scholar 

  11. K. J. Singh, K. Thongam and T. De, Entropy-based application layer DDoS attack detection using artificial neural networks, Entropy, Vol. 18, pp. 350, 2016.

    Article  Google Scholar 

  12. Y. N. Soe, Y. Feng, P. I. Santosa, R. Hartanto and K. Sakurai, Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features, Electronics, Vol. 9, No. 1, pp. 144, 2020.

    Article  Google Scholar 

  13. A. Samy, H. Yu and H. Zhang, Fog-based attack detection framework for internet of things using deep learning, IEEE Access, Vol. 8, pp. 74571–74585, 2020.

    Article  Google Scholar 

  14. T. Gopalakrishnan, D. Ruby, F. Al-Turjman, D. Gupta, I. V. Pustokhina, D. A. Pustokhin and K. Shankar, Deep learning enabled data offloading with cyber attack detection model in mobile edge computing systems, IEEE Access, Vol. 8, pp. 185938–185949, 2020.

    Article  Google Scholar 

  15. P. Kumar, G. P. Gupta and R. Tripathi, Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for iot networks, Arabian Journal for Science and Engineering, Vol. 46, No. 4, pp. 3749–3778, 2021.

    Article  Google Scholar 

  16. G. S. Chhabra, V. P. Singh and M. Singh, Cyber forensics framework for big data analytics in IoT environment using machine learning, Multimedia Tools and Applications, Vol. 79, No. 23, pp. 15881–15900, 2020.

    Article  Google Scholar 

  17. Z. E. Huma, S. Latif, J. Ahmad, Z. Idress, A. Ibrar, Z. Zou, F. Alqahanti and F. Baothman, A hybrid deep random neural network for cyberattack detection in the industrial internet of things, IEEE Access, Vol. 9, pp. 55595–55605, 2021.

    Article  Google Scholar 

  18. S. Venugopal, G. W. Sathianesan and R. Rengaswamy, Cyber forensic framework for big data analytics using Sunflower Jaya optimization-based Deep stacked autoencoder, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2021. https://doi.org/10.1002/jnm.2892.

    Article  Google Scholar 

  19. A. Karimi, S. Abbasabadei, J. A. Torkestani and F. Zarafshan, Cybercrime detection using semi-supervised neural network, Computer Science Journal of Moldova, Vol. 86, No. 2, pp. 155–183, 2021.

    MathSciNet  Google Scholar 

  20. A. V. Dhumane and R. S. Prasad, Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT, Wireless networks, Vol. 25, No. 1, pp. 399–413, 2019.

    Article  Google Scholar 

  21. S. Krinidis and V. Chatzis, A robust fuzzy local information C-means clustering algorithm, IEEE transactions on image processing, Vol. 19, No. 5, pp. 1328–1337, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  22. K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann and R. Wolf, Training deep quantum neural networks, Nature communications, Vol. 11, No. 1, pp. 1–6, 2020.

    Article  Google Scholar 

  23. M. M. Hassan, M. G. R. Alam, M. Z. Uddin, S. Huda, A. Almogren and G. Fortino, Human emotion recognition using deep belief network architecture, Information Fusion, Vol. 51, pp. 10–18, 2019.

    Article  Google Scholar 

  24. P. R. Bhaladhare and D. C. Jinwala, A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm, Advances in Computer Engineering, 2014. https://doi.org/10.1155/2014/396529.

    Article  Google Scholar 

  25. K. Zervoudakis and S. Tsafarakis, A mayfly optimization algorithm, Computers & Industrial Engineering, Vol. 145, pp. 106559, 2020.

    Article  Google Scholar 

  26. Kaveh, A. and Zaerreza, A., “Shuffled shepherd optimization method: a new meta-heuristic algorithm”, Engineering Computations, 2020

  27. UCSD Network Telescope Aggregrated DDoS Metadata, https://catalog.caida.org/details/dataset/telescope_ddos, Accessed on January 2022

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Authors and Affiliations

  1. Department of IT, Lincoln University College, Petaling Jaya, Malaysia

    Suman Thapaliya

  2. Department of Faculty of Science Health and Technology, Nepal Open University, Lalitpur, Nepal

    Pawan Kumar Sharma

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  1. Suman Thapaliya
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  2. Pawan Kumar Sharma
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Correspondence to Suman Thapaliya.

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Thapaliya, S., Sharma, P.K. Cyber Forensic Investigation in IoT Using Deep Learning Based Feature Fusion in Big Data. Int J Wireless Inf Networks 30, 16–29 (2023). https://doi.org/10.1007/s10776-022-00586-3

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  • DOI: https://doi.org/10.1007/s10776-022-00586-3

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