Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph.

Feng, X; Zhang, Y; Meng, MH; Li, Y; Joe, CE; Wang, Z; Bai, G

Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph.

Feng, X Zhang, Y

Meng, MH Li, Y Joe, CE Wang, Z Bai, G

Permalink

Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: ISA Trans, 2023, 141, pp. 10-19
Issue Date:: 2023-10

Closed Access

	Filename	Description	Size
	23‘ ISA Contradiction detection IoT specification.pdf	Published version	511.27 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Feng, X
dc.contributor.author	Zhang, Y https://orcid.org/0000-0001-5611-3483
dc.contributor.author	Meng, MH
dc.contributor.author	Li, Y
dc.contributor.author	Joe, CE
dc.contributor.author	Wang, Z
dc.contributor.author	Bai, G
dc.date.accessioned	2024-02-01T00:42:59Z
dc.date.available	2023-04-21
dc.date.available	2024-02-01T00:42:59Z
dc.date.issued	2023-10
dc.identifier.citation	ISA Trans, 2023, 141, pp. 10-19
dc.identifier.issn	0019-0578
dc.identifier.issn	1879-2022
dc.identifier.uri	http://hdl.handle.net/10453/175172
dc.description.abstract	Due to the boom of Internet of Things (IoT) in recent years, various IoT devices are connected to the Internet and communicate with each other through network protocols such as the Constrained Application Protocol (CoAP). These protocols are typically defined and described in specification documents, such as Request for Comments (RFC), which are written in natural or semi-formal languages. Since developers largely follow the specification documents when implementing web protocols, they have become the de facto protocol specifications. Therefore, it must be ensured that the descriptions in them are consistent to avoid technological issues, incompatibility, security risks, or even legal concerns. In this work, we propose Neural RFC Knowledge Graph (NRFCKG), a neural network-generated knowledge graph based contradictions detection tool for IoT protocol specification documents. Our approach can automatically parse the specification documents and construct knowledge graphs from them through entity extraction, relation extraction, and rule extraction with large language models. It then conducts an intra-entity and inter-entity contradiction detection over the generated knowledge graph. We implement NRFCKG and apply it to the most extensively used messaging protocols in IoT, including the main RFC (RFC7252) of CoAP, the specification document of MQTT, and the specification document of AMQP. Our evaluation shows that NRFCKG generalizes well to other specification documents and it manages to detect contradictions from these IoT protocol specification documents.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	ISA Trans
dc.relation.isbasedon	10.1016/j.isatra.2023.04.025
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering, 0910 Manufacturing Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph.
dc.type	Journal Article
utslib.citation.volume	141
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0910 Manufacturing Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-01T00:42:58Z
pubs.publication-status	Published
pubs.volume	141

Abstract:

Due to the boom of Internet of Things (IoT) in recent years, various IoT devices are connected to the Internet and communicate with each other through network protocols such as the Constrained Application Protocol (CoAP). These protocols are typically defined and described in specification documents, such as Request for Comments (RFC), which are written in natural or semi-formal languages. Since developers largely follow the specification documents when implementing web protocols, they have become the de facto protocol specifications. Therefore, it must be ensured that the descriptions in them are consistent to avoid technological issues, incompatibility, security risks, or even legal concerns. In this work, we propose Neural RFC Knowledge Graph (NRFCKG), a neural network-generated knowledge graph based contradictions detection tool for IoT protocol specification documents. Our approach can automatically parse the specification documents and construct knowledge graphs from them through entity extraction, relation extraction, and rule extraction with large language models. It then conducts an intra-entity and inter-entity contradiction detection over the generated knowledge graph. We implement NRFCKG and apply it to the most extensively used messaging protocols in IoT, including the main RFC (RFC7252) of CoAP, the specification document of MQTT, and the specification document of AMQP. Our evaluation shows that NRFCKG generalizes well to other specification documents and it manages to detect contradictions from these IoT protocol specification documents.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/175172