Journal of Robotics and Automation Research(JRAR)

dslite.ai, Yorba Linda CA 92886, USA

Publications

• Research Article   
  Strategic Defense in Machine Learning: Assessing the Most Optimal Defense Approach to Mitigate Adversarial Cyber Attacks
  Author(s): Jay Kim*
  
  In the era of AI proliferation, developing robust defense mechanisms against adversarial cyberattacks is critical. This project focuses on identifying and evaluating the most effective defense strategy to protect AI models from adversarial attacks. To mitigate overfitting, the baseline AI model was constructed with 2 convolutional layers, a dense layer of 256 nodes, pooling, and dropout layers. This foundational model demonstrated exceptional proficiency, achieving a 99.5% accuracy rate on the Modified National Institute of Standards and Technology (MNIST) dataset. The next three defense methodologies: adversarial training (integrating perturbed images into the training regimen), defensive distillation (employing softened probability distributions to enhance data generalization), and gradient masking (nullifying unused gradients to obscure potential attack vectors) were explored. Each.. Read More»
  

  Abstract HTML PDF