Multi-Source Knowledge Reasoning for Data-Driven IoT Security

Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation aw...

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Autores principales:	Shuqin Zhang, Guangyao Bai, Hong Li, Peipei Liu, Minzhi Zhang, Shujun Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	IoT security threat analysis ontology knowledge reasoning inference rules Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ee4b00056aa240c2a48b5b2a50d9963c
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spelling	oai:doaj.org-article:ee4b00056aa240c2a48b5b2a50d9963c2021-11-25T18:57:35ZMulti-Source Knowledge Reasoning for Data-Driven IoT Security10.3390/s212275791424-8220https://doaj.org/article/ee4b00056aa240c2a48b5b2a50d9963c2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7579https://doaj.org/toc/1424-8220Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation awareness and threat assessment difficult. In this paper, we integrate vulnerabilities, weaknesses, affected platforms, tactics, attack techniques, and attack patterns into a coherent set of links. In addition, we propose an IoT security ontology model, namely, the IoT Security Threat Ontology (IoTSTO), to describe the elements of IoT security threats and design inference rules for threat analysis. This IoTSTO expands the current knowledge domain of cyber security ontology modeling. In the IoTSTO model, the proposed multi-source knowledge reasoning method can perform the following tasks: assess the threats of the IoT environment, automatically infer mitigations, and separate IoT nodes that are subject to specific threats. The method above provides support to security managers in their deployment of security solutions. This paper completes the association of current public knowledge bases for IoT security and solves the semantic heterogeneity of multi-source knowledge. In this paper, we reveal the scope of public knowledge bases and their interrelationships through the multi-source knowledge reasoning method for IoT security. In conclusion, the paper provides a unified, extensible, and reusable method for IoT security analysis and decision making.Shuqin ZhangGuangyao BaiHong LiPeipei LiuMinzhi ZhangShujun LiMDPI AGarticleIoT securitythreat analysisontologyknowledge reasoninginference rulesChemical technologyTP1-1185ENSensors, Vol 21, Iss 7579, p 7579 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	IoT security threat analysis ontology knowledge reasoning inference rules Chemical technology TP1-1185
spellingShingle	IoT security threat analysis ontology knowledge reasoning inference rules Chemical technology TP1-1185 Shuqin Zhang Guangyao Bai Hong Li Peipei Liu Minzhi Zhang Shujun Li Multi-Source Knowledge Reasoning for Data-Driven IoT Security
description	Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation awareness and threat assessment difficult. In this paper, we integrate vulnerabilities, weaknesses, affected platforms, tactics, attack techniques, and attack patterns into a coherent set of links. In addition, we propose an IoT security ontology model, namely, the IoT Security Threat Ontology (IoTSTO), to describe the elements of IoT security threats and design inference rules for threat analysis. This IoTSTO expands the current knowledge domain of cyber security ontology modeling. In the IoTSTO model, the proposed multi-source knowledge reasoning method can perform the following tasks: assess the threats of the IoT environment, automatically infer mitigations, and separate IoT nodes that are subject to specific threats. The method above provides support to security managers in their deployment of security solutions. This paper completes the association of current public knowledge bases for IoT security and solves the semantic heterogeneity of multi-source knowledge. In this paper, we reveal the scope of public knowledge bases and their interrelationships through the multi-source knowledge reasoning method for IoT security. In conclusion, the paper provides a unified, extensible, and reusable method for IoT security analysis and decision making.
format	article
author	Shuqin Zhang Guangyao Bai Hong Li Peipei Liu Minzhi Zhang Shujun Li
author_facet	Shuqin Zhang Guangyao Bai Hong Li Peipei Liu Minzhi Zhang Shujun Li
author_sort	Shuqin Zhang
title	Multi-Source Knowledge Reasoning for Data-Driven IoT Security
title_short	Multi-Source Knowledge Reasoning for Data-Driven IoT Security
title_full	Multi-Source Knowledge Reasoning for Data-Driven IoT Security
title_fullStr	Multi-Source Knowledge Reasoning for Data-Driven IoT Security
title_full_unstemmed	Multi-Source Knowledge Reasoning for Data-Driven IoT Security
title_sort	multi-source knowledge reasoning for data-driven iot security
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ee4b00056aa240c2a48b5b2a50d9963c
work_keys_str_mv	AT shuqinzhang multisourceknowledgereasoningfordatadriveniotsecurity AT guangyaobai multisourceknowledgereasoningfordatadriveniotsecurity AT hongli multisourceknowledgereasoningfordatadriveniotsecurity AT peipeiliu multisourceknowledgereasoningfordatadriveniotsecurity AT minzhizhang multisourceknowledgereasoningfordatadriveniotsecurity AT shujunli multisourceknowledgereasoningfordatadriveniotsecurity
_version_	1718410499224240128

Multi-Source Knowledge Reasoning for Data-Driven IoT Security

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