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The need to implement multilayered security frameworks, integrating strong encryption, authentication mechanisms, and advanced anomaly detection mechanisms to safeguard WBAN systems is emphasized, focusing on security in WBAN.
This survey research work proposes a lifecycle-based understanding of AI security threats and proposed a unified taxonomy for the four major categories of threats observed in real-world settings, namely, data poisoning and backdoor attacks on learning model updates, adversarial attacks on model outputs through input manipulation, privacy leakage through model-based queries, and model extraction for intellectual property theft and creation of rogue replicas of learning models.
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