Integration of security testing into QA pipelines using adversarial ML

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Key Points

This study aims to improve the resilience of machine learning systems against cyber threats through integrated testing.
Theoretical systematisation of machine learning in cyber defence
Analysis of strategies against adversarial attacks
Case study on integration into quality assurance and machine learning operations
Automation of threat detection and response demonstrated effectiveness in handling network anomalies and anti-fraud measures.
Adversarial machine learning identified three attack scenarios with emphasized risks in data quality and privacy.
Integration of multi-level protection strategies into the machine learning lifecycle improved security outcomes.

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Study integrates resilience testing in cyber defence, suggesting enhanced security for machine learning systems.

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Anatolii Husakovskyi (2026) studied this question.

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