Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models
subject: Adversarial attacks; Adversarial defense; Computational efficiency; Model Robustness
description: 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.
creator: Kalaiselvi, K.; Khundongbam, Alex; Steffyn, Kezya; Mangaiyarkarasi, T.
source: Studies in Systems, Decision and Control;Volume;645;pp.241-258
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 21984182;
language: English
type: Book chapter
coverage: 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