Abstract
The rapid advancement of Internet of Things (IoT) technology has brought about new challenges in the field of digital forensics. Traditional forensic methods are often inadequate in dealing with the decentralized and distributed nature of IoT devices. IoT has several advantages that have made it appealing to consumers and attackers alike. The technology and resources available to today’s cybercriminals allow them to launch millions of sophisticated attacks. In this paper, we propose a blockchain-based forensic model (BBFMI) for investigating IoT devices. The proposed BBFMI model utilizes the immutability and tamper-proof features of blockchain technology to provide a secure and reliable way of collecting, storing, and analyzing forensic evidence from IoT devices. One of the most important benefits of BBFMI is that it provides digital forensics investigators with an immutable chain of evidence that can be used to trace the source of data and its subsequent changes. For instance, when using BBFMI, forensic investigators can easily trace the chain of events that led to the data breach of an IoT device. Our results show that the proposed blockchain-based forensic approach can provide a secure, efficient, and tamper-proof solution for investigating IoT devices in digital crime scenes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Okoh, S.A., Dibal, P.Y.: Performance analysis of IoT cloud-based platforms using quality of service metrics. Int. J. Wirel. Microw. Technol. 13, 1–4 (2020). https://doi.org/10.5815/IJWMT.2023.01.05
Jain, R., Tata, S.: Cloud to edge: distributed deployment of process-aware IoT applications. In: Proceedings - 2017 IEEE 1st International Conference on Edge Computing, EDGE 2017, pp. 182–189 (2017). https://doi.org/10.1109/IEEE.EDGE.2017.32
Li, D., Peng, W., Deng, W., Gai, F.: A blockchain-based authentication and security mechanism for IoT. In: Proceedings - International Conference on Computer Communications and Networks, ICCCN. Institute of Electrical and Electronics Engineers Inc (2018). https://doi.org/10.1109/ICCCN.2018.8487449
Zhao, Y., et al.: Privacy-preserving blockchain-based federated learning for IoT Devices. IEEE Internet Things J. 8, 1817–1829 (2021). https://doi.org/10.1109/JIOT.2020.3017377
Gong, L., Alghazzawi, D.M., Cheng, L.: Bcot sentry: a blockchain-based identity authentication framework for IoT devices. Information 12, 203 (2021). https://doi.org/10.3390/info12050203
Agrawal, S., Kumar, S.: MLSMBQS: design of a machine learning based split & merge blockchain model for QoS-aware secure IoT deployments. Int. J. Image Graph. Signal Process. 14, 58–71 (2022). https://doi.org/10.5815/IJIGSP.2022.05.05
Mishra, A., Singh, C., Dwivedi, A., Singh, D., Biswal, A.K.: Network forensics: an approach towards detecting cyber crime. In: 2021 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2021. Institute of Electrical and Electronics Engineers Inc (2021). https://doi.org/10.1109/APSIT52773.2021.9641399
Motha, J., Maduranga, M., Jayatilaka, N.: Design of an IoT-enabled solar tracking system for smart farms. Int. J. Wirel. Microw. Technol. 12, 1–13 (2022). https://doi.org/10.5815/IJWMT.2022.06.01
James, J.I., Shosha, A.F., Gladyshev, P.: Digital forensic investigation and cloud computing. In: Cloud Technology: Concepts, Methodologies, Tools, and Applications, pp. 1231–1271. IGI Global (2014). https://doi.org/10.4018/978-1-4666-6539-2.ch057
Atlam, H.F., Alenezi, A., Walters, R.J., Wills, G.B., Daniel, J.: Developing an adaptive risk-based access control model for the internet of things. In: Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017, pp. 655–661 (2018). https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.103
Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., Markakis, E.K.: A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues (2020) https://ieeexplore.ieee.org/abstract/document/8950109/. https://doi.org/10.1109/COMST.2019.2962586
Rauf, A., Shaikh, R.A., Shah, A.: Trust modelling and management for IoT healthcare. Int. J. Wirel. Microw. Technol. 12, 21–35 (2022). https://doi.org/10.5815/IJWMT.2022.05.03
Li, S., Choo, K.K.R., Sun, Q., Buchanan, W.J., Cao, J.: IoT forensics: amazon echo as a use case. IEEE Internet Things J. 6, 6487–6497 (2019). https://doi.org/10.1109/JIOT.2019.2906946
Nieto, A., Rios, R., Lopez, J.: IoT-forensics meets privacy: towards cooperative digital investigations. Sens. (Switz.) 18, 492 (2018). https://doi.org/10.3390/s18020492
Nieto, A., Rios, R., Lopez, J.: A methodology for privacy-aware IoT-forensics. In: Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, pp. 626–633 (2017). https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.293
Noura, H.N., Salman, O., Chehab, A., Couturier, R.: DistLog: a distributed logging scheme for IoT forensics. Ad Hoc Netw. 98, 102061 (2020). https://doi.org/10.1016/j.adhoc.2019.102061
Kumar, G., Saha, R., Lal, C., Conti, M.: Internet-of-Forensic (IoF): a blockchain based digital forensics framework for IoT applications. Futur. Gener. Comput. Syst. 120, 13–25 (2021). https://doi.org/10.1016/j.future.2021.02.016
Gao, H., et al.: BlockchainBot: a novel botnet infrastructure enhanced by blockchain technology and IoT. Electron. 11, 1065 (2022). https://doi.org/10.3390/electronics11071065
Novo, O.: Blockchain meets IoT: an architecture for scalable access management in IoT. IEEE Internet Things J. 5, 1184–1195 (2018). https://doi.org/10.1109/JIOT.2018.2812239
Atlam, H.F., Alenezi, A., Alassafi, M.O., Wills, G.B.: Blockchain with Internet of Things: benefits, challenges, and future directions. Int. J. Intell. Syst. Appl. 10, 40–48 (2018). https://doi.org/10.5815/ijisa.2018.06.05
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, C., Khajuria, H., Nayak, B.P. (2023). A Study of Implementing a Blockchain-Based Forensic Model Integration (BBFMI) for IoT Devices in Digital Forensics. In: Hu, Z., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education VI. ICCSEEA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-031-36118-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-36118-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36117-3
Online ISBN: 978-3-031-36118-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)