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Adversarial attacks to machine learning based models in cybersecurity

Modern Intrusion Detection Systems (IDSs) rely on machine learning for detecting and defending cyber-attacks in information technology (IT) networks. However, the introduction of such systems has introduced an additional attack dimension; the trained IDS models may also be subject to attacks.The act of deploying attacks towards machine learning based systems is known as Adversarial Machine Learning (AML) [1]. The aim is to exploit the weaknesses of the pretrained model which has “blind spots” between data points it has seen during …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty of Science
School of Computer Science

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