Abstract:
The fast-changing realm of machine learning and artificial intelligence makes model susceptibility to adversarial assaults a serious concern. Adversarial assaults manipul...Show MoreMetadata
Abstract:
The fast-changing realm of machine learning and artificial intelligence makes model susceptibility to adversarial assaults a serious concern. Adversarial assaults manipulate input data to influence machine learning model estimations. This threatens self-driving vehicles, healthcare, and cybersecurity. To solve this challenge, we investigate how unsupervised learning might prevent adversarial assaults. Our paper introduces the AutoEncoder-Based Adversarial Detector (AED), Variational Autoencoders for Adversarial Feature Extraction (VAE-AFE), and Clustering and Density-Based Hybrid Defense. These strategies increase machine learning security via uncontrolled learning. Because they recreate raw data, extract strong traits, and apply clustering and density-based techniques, these methods discover and reduce adversarial hazards well. We demonstrate that these techniques outperform adversarial defensive tactics in prolonged trials. Accuracy, precision, recall, F1 Score, and ROC AUC reveal that the recommended techniques increase over time. The offered solutions offer a clear defense against dynamic threats, surpassing previous ways and securing AI applications. As artificial intelligence becomes increasingly widespread, machine learning model security and integrity are crucial. These approaches offer promise for this objective and valuable insights into adversarial defense, a burgeoning field.
Published in: 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0
Date of Conference: 05-07 June 2024
Date Added to IEEE Xplore: 30 September 2024
ISBN Information: