Jun Hong's Outputs (4)

ProxiMix: Enhancing fairness with proximity samples in subgroups (2024)
Presentation / Conference Contribution

Many bias mitigation methods have been developed for addressing fairness issues in machine learning. We have found that using linear mixup alone, a data augmentation technique, for bias mitigation, can still retain biases present in dataset labels. R... Read More about ProxiMix: Enhancing fairness with proximity samples in subgroups.

Improving search space analysis of fuzzing mutators using cryptographic structures (2024)
Presentation / Conference Contribution

This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the exis... Read More about Improving search space analysis of fuzzing mutators using cryptographic structures.

AdaHAT: Adaptive hard attention to the task in task-incremental learning (2024)
Presentation / Conference Contribution

Catastrophic forgetting is a major issue in task-incremental learning, where a neural network loses what it has learned in previous tasks after being trained on new tasks. A number of architecture- based approaches have been proposed to address this... Read More about AdaHAT: Adaptive hard attention to the task in task-incremental learning.

Vulnerability detection through machine learning-based fuzzing: A systematic review (2024)
Journal Article

Modern software and networks underpin our digital society, yet the rapid growth of vulnerabilities that are uncovered within these threaten our cyber security posture. Addressing these issues at scale requires automated proactive approaches that can... Read More about Vulnerability detection through machine learning-based fuzzing: A systematic review.