Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks

Chat-based Social Engineering (CSE) is widely recognized as a key factor to successful cyber-attacks, especially in small and medium-sized enterprise (SME) environments. Despite the interest in preventing CSE attacks, few studies have considered the specific features of the language used by the atta...

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Autores principales:	Nikolaos Tsinganos, Ioannis Mavridis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cybersecurity sensitive data social engineering corpus annotation chat-based attack Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/c99db71197604b4d8070cefbc01d5bce
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id	oai:doaj.org-article:c99db71197604b4d8070cefbc01d5bce
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spelling	oai:doaj.org-article:c99db71197604b4d8070cefbc01d5bce2021-11-25T16:39:36ZBuilding and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks10.3390/app1122108712076-3417https://doaj.org/article/c99db71197604b4d8070cefbc01d5bce2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10871https://doaj.org/toc/2076-3417Chat-based Social Engineering (CSE) is widely recognized as a key factor to successful cyber-attacks, especially in small and medium-sized enterprise (SME) environments. Despite the interest in preventing CSE attacks, few studies have considered the specific features of the language used by the attackers. This work contributes to the area of early-stage automated CSE attack recognition by proposing an approach for building and annotating a specific-purpose corpus and presenting its application in the CSE domain. The resulting CSE corpus is then evaluated by training a bi-directional long short-term memory (bi-LSTM) neural network for the purpose of named entity recognition (NER). The results of this study emphasize the importance of adding a plethora of metadata to a dataset to provide critical in-context features and produce a corpus that broadens our understanding of the tactics used by social engineers. The outcomes can be applied to dedicated cyber-defence mechanisms utilized to protect SME employees using Electronic Medium Communication (EMC) software.Nikolaos TsinganosIoannis MavridisMDPI AGarticlecybersecuritysensitive datasocial engineeringcorpusannotationchat-based attackTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10871, p 10871 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cybersecurity sensitive data social engineering corpus annotation chat-based attack Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	cybersecurity sensitive data social engineering corpus annotation chat-based attack Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Nikolaos Tsinganos Ioannis Mavridis Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks
description	Chat-based Social Engineering (CSE) is widely recognized as a key factor to successful cyber-attacks, especially in small and medium-sized enterprise (SME) environments. Despite the interest in preventing CSE attacks, few studies have considered the specific features of the language used by the attackers. This work contributes to the area of early-stage automated CSE attack recognition by proposing an approach for building and annotating a specific-purpose corpus and presenting its application in the CSE domain. The resulting CSE corpus is then evaluated by training a bi-directional long short-term memory (bi-LSTM) neural network for the purpose of named entity recognition (NER). The results of this study emphasize the importance of adding a plethora of metadata to a dataset to provide critical in-context features and produce a corpus that broadens our understanding of the tactics used by social engineers. The outcomes can be applied to dedicated cyber-defence mechanisms utilized to protect SME employees using Electronic Medium Communication (EMC) software.
format	article
author	Nikolaos Tsinganos Ioannis Mavridis
author_facet	Nikolaos Tsinganos Ioannis Mavridis
author_sort	Nikolaos Tsinganos
title	Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks
title_short	Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks
title_full	Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks
title_fullStr	Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks
title_full_unstemmed	Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks
title_sort	building and evaluating an annotated corpus for automated recognition of chat-based social engineering attacks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c99db71197604b4d8070cefbc01d5bce
work_keys_str_mv	AT nikolaostsinganos buildingandevaluatinganannotatedcorpusforautomatedrecognitionofchatbasedsocialengineeringattacks AT ioannismavridis buildingandevaluatinganannotatedcorpusforautomatedrecognitionofchatbasedsocialengineeringattacks
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Building and Evaluating an Annotated Corpus for Automated Recognition of Chat-Based Social Engineering Attacks

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