A Reactive Security Framework for Protecting AI Models from Adversarial Attacks: An Autoencoder-Based Approach

Author(s)	Vasudhevan Sudharsanan
Country	India
Abstract	This paper proposes a reactive security framework for enhancing the resilience of AI models against adversarial attacks [5, 6, 7, 8]. The framework leverages runtime monitoring, anomaly detection, and model retraining to dynamically adapt to evolving attack strategies. Anomaly detection is performed using an autoencoder-based algorithm that identifies deviations from expected model behavior [8, 9, 10]. Model retraining employs adversarial training to ”immunize” the model against similar attacks [5, 6]. We discuss the choice of autoencoder architectures for different data types and detail the mathematical foundations of both anomaly detection and adversarial training [3]. The framework’s effectiveness is evaluated through simulations and benchmark datasets, demonstrating its ability to secure AI models against diverse adversarial attacks.
Field	Computer > Network / Security
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-10
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.32434

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