Eloff, Jan H.P.2025-01-152025-01-152025-05-272024-12-13*A2025http://hdl.handle.net/2263/100064Thesis (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.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.UCTDSustainable Development Goals (SDGs)BotsAnomaly detectionMachine learningCybersecurityCyber threat intelligenceThesisu1525642210.25403/UPresearchdata.28024112