Anomaly Detection in IoT Devices Using Quantum Machine Learning

Karim, Shaikh Faiyaz; Chowdhury, Sajid Ahmed; Bhuiyan, Md. Shahriar Islam

dc.contributor.author	Karim, Shaikh Faiyaz
dc.contributor.author	Chowdhury, Sajid Ahmed
dc.contributor.author	Bhuiyan, Md. Shahriar Islam
dc.date.accessioned	2025-03-10T05:30:48Z
dc.date.available	2025-03-10T05:30:48Z
dc.date.issued	2024-06-24
dc.identifier.citation	[1] A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood, and A. Anwar, “Ton_iot teleme try dataset: A new generation dataset of iot and iiot for data-driven intrusion detection systems,” Ieee Access, vol. 8, pp. 165 130–165 150, 2020. [2] S. Altares-López, A. Ribeiro, and J. J. García-Ripoll, “Automatic design of quan tum feature maps,” Quantum Science and Technology, vol. 6, no. 4, p. 045 015, Aug. 2021, issn: 2058-9565. doi: 10.1088/2058-9565/ac1ab1. [Online]. Avail able: http://dx.doi.org/10.1088/2058-9565/ac1ab1. [3] V. Bolon-Canedo, N. Sanchez-Marono, and A. Alonso-Betanzos, “Feature selec tion and classification in multiple class datasets: An application to kdd cup 99 dataset,” Expert Systems with Applications, vol. 38, no. 5, pp. 5947–5957, 2011. [4] K. DeMedeiros, A. Hendawi, and M. Alvarez, “A survey of ai-based anomaly detection in iot and sensor networks,” Sensors, vol. 23, no. 3, p. 1352, 2023. [5] Y. Du, T. Huang, S. You, M.-H. Hsieh, and D. Tao, “Quantum circuit architecture search for variational quantum algorithms,” npj Quantum Information, vol. 8, no. 1, p. 62, 2022. [6] M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, I. Ali, and M. Guizani, “A sur vey of machine and deep learning methods for internet of things (iot) security,” IEEE Communications Surveys Amp; Tutorials, vol. 22, pp. 1646–1685, 3 2020. doi: 10.1109/comst.2020.2988293. [7] J. Goh, S. Adepu, K. N. Junejo, and A. Mathur, “A dataset to support research in the design of secure water treatment systems,” in Critical Information Infras tructures Security: 11th International Conference, CRITIS 2016, Paris, France, October 10–12, 2016, Revised Selected Papers 11, Springer, 2017, pp. 88–99. [8] X. Guo, T. Muta, and J. Zhao, “Quantum circuit ansatz: Abstraction and reuse of quantum algorithm design,” arXiv preprint arXiv:2405.05021, 2024. [9] R. Gurunath, M. Agarwal, A. Nandi, and D. Samanta, “An overview: Security issue in iot network,” in 2018 2nd international conference on I-SMAC (IoT in social, Mobile, analytics and cloud)(I-SMAC) I-SMAC (IoT in social, Mobile, an 43 alytics and cloud)(I-SMAC), 2018 2nd international conference on, IEEE, 2018, pp. 104–107. [10] H. Kayan, Y. Majib, W. Alsafery, M. Barhamgi, and C. Perera, “Anoml-iot: An end to end re-configurable multi-protocol anomaly detection pipeline for inter net of things,” Internet of Things, vol. 16, p. 100 437, 2021. [11] J. S. Kumar and D. R. Patel, “A survey on internet of things: Security and privacy issues,” International Journal of Computer Applications, vol. 90, no. 11, 2014. [12] P. Kushwaha, H. Buckchash, and B. Raman, “Anomaly based intrusion detec tion using filter based feature selection on kdd-cup 99,” in TENCON 2017-2017 IEEE Region 10 Conference, IEEE, 2017, pp. 839–844. [13] A. Nakayama, K. Mitarai, L. Placidi, T. Sugimoto, and K. Fujii, Vqe-generated quantum circuit dataset for machine learning, 2023. arXiv: 2302.09751 [quant-ph]. [14] A. Nazir, S. Sholla, and A. Bashir, “Internet of things security: Issues, challenges and counter-measures,” International Journal of Network and Technology, vol. 7, no. 3, 2019. [15] A. Pellow-Jarman, I. Sinayskiy, A. Pillay, and F. Petruccione, “A comparison of various classical optimizers for a variational quantum linear solver,” Quantum Information Processing, vol. 20, no. 6, Jun. 2021, issn: 1573-1332. doi: 10.1007/ s11128-021-03140-x. [Online]. Available: http://dx.doi.org/10.1007/ s11128-021-03140-x. [16] J. Qin, “Review of ansatz designing techniques for variational quantum algo rithms,” in Journal of Physics: Conference Series, IOP Publishing, vol. 2634, 2023, p. 012 043. [17] S. K. Satpathy, V. Vibhu, B. K. Behera, S. Al-Kuwari, S. Mumtaz, and A. Farouk, “Analysis of quantum machine learning algorithms in noisy channels for classi fication tasks in the iot extreme environment,” IEEE Internet of Things Journal, 2023. [18] S. K. Satpathy, V. Vibhu, B. K. Behera, S. Al-Kuwari, S. Mumtaz, and A. Farouk, “Analysis of quantum machine learning algorithms in noisy channels for classi fication tasks in the iot extreme environment,” IEEE Internet of Things Journal, 2023. [19] T. Sharma and R. A. H. Khan, “Optimizing network security using lstm algo rithm for traffic classification on unswnb15 and kddcup99 dataset,” Interna tional Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 8s, pp. 671–682, 2024. 44 [20] S. Sim, P. D. Johnson, and A. Aspuru-Guzik, “Expressibility and entangling ca pability of parameterized quantum circuits for hybrid quantum-classical algo rithms,” Advanced Quantum Technologies, vol. 2, no. 12, Oct. 2019, issn: 2511- 9044. doi: 10.1002/qute.201900070. [Online]. Available: http://dx.doi. org/10.1002/qute.201900070. [21] D. Srinivasan, K. Chakrabarti, N. Chopra, and A. Dutt, Quantum circuit opti mization through iteratively pre-conditioned gradient descent, 2023. arXiv: 2309. 09957 [quant-ph]. [22] O. C. Stoica, Born rule: Quantum probability as classical probability, 2023. arXiv: 2209.08621 [quant-ph]. [23] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the kdd cup 99 data set,” in 2009 IEEE symposium on computational intelligence for security and defense applications, Ieee, 2009, pp. 1–6. [24] Y. Tjandra and H. S. Sugiarto, “An evolutionary algorithm design for pauli based quantum kernel classification,” 2022. [25] I. P. Turnipseed, A new scada dataset for intrusion detection research. Mississippi State University, 2015. [26] N. Veyrat-Charvillon and F.-X. Standaert, “Mutual information analysis: How, when and why?” In International Workshop on Cryptographic Hardware and Embedded Systems, Springer, 2009, pp. 429–443. [27] Y.-Y. Yang, C. Rashtchian, H. Zhang, R. R. Salakhutdinov, and K. Chaudhuri, “A closer look at accuracy vs. robustness,” Advances in neural information pro cessing systems, vol. 33, pp. 8588–8601, 2020. [28] A. Yazdinejad, M. Kazemi, R. M. Parizi, A. Dehghantanha, and H. Karimipour, “An ensemble deep learning model for cyber threat hunting in industrial inter net of things,” Digital Communications and Networks, vol. 9, no. 1, pp. 101–110, 2023. [29] A. Zeguendry, Z. Jarir, and M. Quafafou, “Quantum machine learning: A re view and case studies,” Entropy, vol. 25, no. 2, p. 287,	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2367
dc.description	Supervised by Dr. Md. Sakhawat Hossen, Associate Professor, Mr.Ali Abir Shuvro, Lecturer, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Computer Science and Engineering, 2024	en_US
dc.description.abstract	The adoption of the Internet of Things in our daily lives has led to an explosion of data. As we stay connected our data is exposed in various networks which can be a huge security concern. It is important to effectively and efficiently detect these anomalies and act accordingly. However, traditional machine learning techniques struggle to process and analyze this voluminous data efficiently. Quantum Machine Learning (QML) offers a promising solution by leveraging its quantum benefits of exponen tial speedup through superposition. This paper explores the application of QML for anomaly detection in IoT networks, highlighting its potential to significantly improve detection accuracy and speed. Ultimately the goal of this research is to build a foun dation for quantum machine learning algorithms showing its potential equipping us for the future of big data.	en_US
dc.language.iso	en	en_US
dc.title	Anomaly Detection in IoT Devices Using Quantum Machine Learning	en_US
dc.type	Thesis	en_US