Cybersecurity : the intelligent discovery of malicious bots
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University of Pretoria
Abstract
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.
Description
Thesis (PhD (Information Technology))--University of Pretoria, 2024.
Keywords
UCTD, Sustainable Development Goals (SDGs), Bots, Anomaly detection, Machine learning, Cybersecurity, Cyber threat intelligence
Sustainable Development Goals
None
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