Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kalaiselvi, K.; Khundongbam, Alex; Steffyn, Kezya; Mangaiyarkarasi, T. Title A name given to the resource Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Studies in Systems, Decision and Control;Volume;645;pp.241-258 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-10016-0_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-10016-0_20</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026786951?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026786951?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kalaiselvi K., Department of Computer Science, Kristu Jayanti University, Bengaluru, India; Khundongbam A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Steffyn K., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Mangaiyarkarasi T., Department of Management, FOM-MBA SRMIST VDP Campus, Chennai, India Description An account of the resource Adversarial attacks are a serious threat to machine learning models, both for conventional architectures, like neural networks, and for more sophisticated frameworks, like Vision Transformers (ViTs). Although a lot of work has been done to defend state-of-the-art deep learning models against attacks like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Gaussian noise perturbations, classical machine learning models like logistic regression, support vector machines (SVMs), and decision trees are relatively less explored despite their extensive use in situations where low computational complexity and high interpretability are needed. This work presents a rigorous evaluation of the adversarial vulnerability of binary and other classical models on the MNIST dataset and explores the effectiveness of various defense mechanisms, including adversarial training, input pre-processing (Gaussian smoothing), and defensive distillation. Experiments demonstrate that adversarial training is the most effective defense that improves model robustness with classification accuracies of up to 96% in all attack scenarios. In contrast, defensive distillation and input preprocessing make modest gains, with accuracy levels ranging from 61 to 81% based on the nature of the attack. Through adversarial threat analysis of typical machine learning models, this work points out their inherent susceptibility to adversarial perturbations and introduces robust defense techniques. These results identify the necessity for robust security and reaffirm the practical viability of typical models in the scenario of resource-constrained environments, contributing towards a more complete picture of adversarial defenses for the entire spectrum of machine learning architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Adversarial attacks; Adversarial defense; Computational efficiency; Model Robustness Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21984182; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online