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Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning

Over the past years, computer security has been a field of study that assists in protecting one’s information. It has matured over time in fighting against cybercrime in exploiting the technical vulnerabilities of hardware or software. However, there is a kind of attack that particularly exploits th...

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Bibliographic Details
Main Author: Sia, Ken Yen
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.utar.edu.my/4664/1/fyp_CS_2022_SKY.pdf
http://eprints.utar.edu.my/4664/
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spelling my-utar-eprints.46642023-01-15T13:29:28Z Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning Sia, Ken Yen Q Science (General) T Technology (General) Over the past years, computer security has been a field of study that assists in protecting one’s information. It has matured over time in fighting against cybercrime in exploiting the technical vulnerabilities of hardware or software. However, there is a kind of attack that particularly exploits the human psychological weakness in acquiring confidential information is emerging which is called social engineering. It has lesser cost and branches to many variations of type of attacks than the traditional technical approach which challenges organization’s protection such as SMEs. Most of the current detection models only provide a guideline framework in detecting such attacks which is not efficient or with low accuracy. This project aims at building a model that is based on another popular field which is machine learning in detecting attacks. This can be applied to flag a conversation as if it is a social engineering attack. The project will use natural language processing in extracting certain features as the input of an algorithm to generate a reputation score that will be trained using machine learning to build the detection model. The model will be evaluated and validated using datasets by generating the result scores. 2022-04-21 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4664/1/fyp_CS_2022_SKY.pdf Sia, Ken Yen (2022) Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning. Final Year Project, UTAR. http://eprints.utar.edu.my/4664/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Sia, Ken Yen
Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning
description Over the past years, computer security has been a field of study that assists in protecting one’s information. It has matured over time in fighting against cybercrime in exploiting the technical vulnerabilities of hardware or software. However, there is a kind of attack that particularly exploits the human psychological weakness in acquiring confidential information is emerging which is called social engineering. It has lesser cost and branches to many variations of type of attacks than the traditional technical approach which challenges organization’s protection such as SMEs. Most of the current detection models only provide a guideline framework in detecting such attacks which is not efficient or with low accuracy. This project aims at building a model that is based on another popular field which is machine learning in detecting attacks. This can be applied to flag a conversation as if it is a social engineering attack. The project will use natural language processing in extracting certain features as the input of an algorithm to generate a reputation score that will be trained using machine learning to build the detection model. The model will be evaluated and validated using datasets by generating the result scores.
format Final Year Project / Dissertation / Thesis
author Sia, Ken Yen
author_facet Sia, Ken Yen
author_sort Sia, Ken Yen
title Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning
title_short Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning
title_full Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning
title_fullStr Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning
title_full_unstemmed Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning
title_sort social engineering exploitation detection (seed) in malaysia's smes using machine learning
publishDate 2022
url http://eprints.utar.edu.my/4664/1/fyp_CS_2022_SKY.pdf
http://eprints.utar.edu.my/4664/
_version_ 1755876965743591424
score 13.211869

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