Nwakanma, Cosmas Ifeanyi; Chijioke Ahakonye, Love Allen; Nkechinyere Njoku, Judith; Eze, Joy and Kim, Dong-Seong. 2023. 'Effective Industrial Internet of Things Vulnerability Detection Using Machine Learning'. In: 2022 5th Information Technology for Education and Development (ITED). Abuja, Nigeria 1 - 3 November 2022. [Conference or Workshop Item]

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Abstract or Description

Protecting the industrial internet of things (IIoT) devices through vulnerability detection is critical as the consequences of attacks can be devastating. Machine learning (ML) has assisted several works in this regard, improving vulnerability detection accuracy. Based on established vulnerability assessment, development and performance comparison of various ML detection algorithms is essential. This work presents a description of the IIoT protocols and their vulnerabilities. The performance of the ML-based detection system was developed using the WUSTL-IIoT-2018 dataset for industrial control systems (SCADA) cy-bersecurity research. The approach was validated using the ICS-SCADA and CICDDoS2019 datasets, a recent dataset that captures new dimensions of distributed denial of service (DDoS) attacks on networks. The evaluation and validation results show that the proposed scheme could help with high vulnerability detection and mitigation accuracy across all evaluated datasets.

Item Type:	Conference or Workshop Item (Paper)
Identification Number (DOI):	https://doi.org/10.1109/ITED56637.2022.10051622
Additional Information:	“© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Funding: This research work was supported by Priority Research Centers Program through NRF funded by MEST (2018R1A6A1A03024003) and the Grand Information Technology Research Center support program (IITP-2022-2020-001612) supervised by the IITP by MSIT, Korea.
Keywords:	Radio frequency, Performance evaluation, Protocols, Industrial control , Focusing , Intrusion detection , Machine learning
Departments, Centres and Research Units:	Computing
Dates:	Date Event 26 October 2022 Accepted 2 March 2023 Published
Event Location:	Abuja, Nigeria
Date range:	1 - 3 November 2022
Item ID:	38235
Date Deposited:	30 Jan 2025 12:52
Last Modified:	04 Feb 2025 13:41
URI:	https://research.gold.ac.uk/id/eprint/38235

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