ავტორი: Faisal A. Garba, Kabiru I. Kunya, Zahrau Ahmad Zakari, Kunya, Shazali A. Ibrahim, Kunya, Abubakar Abba, Zaharaddeen Karami Lawal, Aliyu Lawan Musa
ორგანიზაცია: Department of Computer Science Education, Sa’adatu Rimi College of Education, Kano, Department of Computer Science, Federal College of Education, Zaria Jameel Shehu Yalli, Federal University Gusau, Department of Computer Science, Federal University Dutse, Department of Computer Engineering Technology, School of Technology, Kano State Polytechnic


საკვანძო სიტყვები: IoT, forensics, blockchain, genetic-fuzzy
აბსტრაქტი. Practitioners of network forensics often employ automated software and hardware tools for the collection and preservation of data, however, the process of performing a forensic examination is not well defined. This has resulted in the emergence of various digital forensic frameworks, which determine the correct course of action during an investigation, separating the process into autonomous stages and suggesting appropriate tools and techniques for each task. Even though many forensic frameworks have been proposed, existing solutions give emphasis on acquisition and neglect examination and analysis. Privacy is also a key element in maintaining the confidentiality of data in forensics as it may lead to exposure of personal identifiable information. Furthermore, accountability is one of the IoT forensics challenges. The widespread adoption of an estimated 30.9 billion IoT devices by 2025 (Statista, 2021), as well as the increasing interconnectivity of those devices to traditional networks, not to mention to one another with the advent of fifth generation (5G) networks, underscore the need for IoT forensics. This work proposed a novel low cost IoT forensic framework to tackle: (a.) the examination and analysis phase of IoT forensics using genetic-fuzzy expert system (b.) the issue of guarding the privacy and chain of custody of IoT forensics data using hyperledger fabric, private-permissioned blockchain that is both free and open source. The framework will be implemented and evaluated with related works using BoT-IoT dataset. The BoT-IoT dataset includes Distributed Denial of Service (DDoS), Denial of Service (DoS), Operating System (OS) and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used. The genetic-fuzzy IoT forensics framework will be compared against related work and Network Forensics Analysis Tool (NFAT) to evaluate the performance and accuracy of the proposed framework. The private permissioned blockchain IoT forensics framework will be compared against a related work to evaluate the security and cost of the proposed private permissioned blockchain framework. The genetic-fuzzy blockchain-enabled IoT forensic framework will be compared with, related works and NFATs to evaluate the speed and accuracy performance of the proposed framework. The result of this study is a low cost genetic-fuzzy blockchain-enabled IoT forensics framework.


1.Atlam, H., Alenezi, A., Alassafi, M. O., Alshdadi, A. A., & Wills, G. B. (2020). Security, Cybercrime and Digital Forensics for IoT. In S.-L. Peng, & S. Pal, Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm (pp. 551 -). Cham, Switzerland: Springer Nature Switzerland AG.
2.Brotsis, S., Kolokotronis, N., Limniotis, K., Shiaeles, S., Kavallieros, D., Bellini, E., & Pavu´e, C. (2019). Blockchain Solutions for Forensic Evidence Preservation in IoT Environments. IEEE NetSoft 2019 - 1st Workshop on Cyber-Security Threats, Trust and Privacy Management in Software-Defined (pp. 110-114). IEEE.
3.Cabrera et al. (2009) Fuzzy Logic, Soft Computing, and Applications.
4.Liao et al. (2009) Network forensics based on fuzzy logic and expert system
5.Datt (2016) et al. Learning network forensics https://www.packtpub.com/product/learning-network-forensics/9781782174905
6.Hossain, M., Karim, Y., & Hasan, R. (2018). FIF-IoT: A Forensic Investigation Framework for IoT Using a Public Digital Ledger. 2018 IEEE International Congress on Internet of Things (ICIOT). IEEE.
7.Jawal et al. (2019) Hands-On Network Forensics
8.Koroniotis, N., & Moustafa, N. (2020). Enhancing Network Forensics with Particle Swarm and Deep Learning: The Particle Deep Framework. arXiv, 1-20.
9.Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019). Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset. Future Generation Computer Systems, 779–796.
10.Li, S., Qin, T., & Min, G. (2019). Blockchain-Based Digital Forensics Investigation Framework in the Internet of Things and Social Systems. IEEE Transactions on Computational Social Systems, 1-9.
11.Lutta, P., Sedky, M., & Hassan, M. (2020). The Forensic Swing of Things: The Current Legal and Technical Challenges of IoT Forensics. World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering, 14(5), 159-165.