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Cybersecurity : the intelligent discovery of malicious bots
Thesis (PhD (Information Technology))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613617702043648 |
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| access_status_str | Open Access |
| author2 | Eloff, Jan H.P. |
| author_browse | Eloff, Jan H.P. |
| author_facet | Eloff, Jan H.P. |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD (Information Technology))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/100064 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:00.149Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/100064 Cybersecurity : the intelligent discovery of malicious bots Eloff, Jan H.P. u15256422@tuks.co.za Mbona, Innocent UCTD Sustainable Development Goals (SDGs) Bots Anomaly detection Machine learning Cybersecurity Cyber threat intelligence Thesis (PhD (Information Technology))--University of Pretoria, 2024. This thesis proposes a methodological approach named CySecML, which provides a framework for developing intelligent ML-based cybersecurity solutions that can assist cyber threat intelligence (CTI) procedures to effectively discover cyber threats launched by bots on IAPs. The CySecML methodology is based on two components - data preparation and the InternetBotDetector model, as it aims to optimise existing techniques that include data quality checks, feature selection and ML on cybersecurity data sets. To provide proof-of-concept of this methodology, two different IAPs namely - online social networks (OSNs) and network intrusion detection systems (NIDSs) were chosen to discover bot cyberattacks. Computer Science PhD (Information Technology) Unrestricted Faculty of Engineering, Built Environment and Information Technology None 2025-01-15T07:39:58Z 2025-01-15T07:39:58Z 2025-05-27 2024-12-13 Thesis * A2025 http://hdl.handle.net/2263/100064 10.25403/UPresearchdata.28024112 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Bots Anomaly detection Machine learning Cybersecurity Cyber threat intelligence Cybersecurity : the intelligent discovery of malicious bots |
| title | Cybersecurity : the intelligent discovery of malicious bots |
| title_full | Cybersecurity : the intelligent discovery of malicious bots |
| title_fullStr | Cybersecurity : the intelligent discovery of malicious bots |
| title_full_unstemmed | Cybersecurity : the intelligent discovery of malicious bots |
| title_short | Cybersecurity : the intelligent discovery of malicious bots |
| title_sort | cybersecurity the intelligent discovery of malicious bots |
| topic | UCTD Sustainable Development Goals (SDGs) Bots Anomaly detection Machine learning Cybersecurity Cyber threat intelligence |
| url | http://hdl.handle.net/2263/100064 |