Sub-headline: BIT researchers introduce Malcom to tackle cross-domain encrypted traffic detection using self-supervised learning.

Deep neural networks (DNNs) are demonstrated to be vulnerable to adversarial examples. Adversarial training is mainstrem method to improve adversarial robustness of DNNs, which augments the training set with adversarial examples and adopts adversarial regularization loss to improve the robustness of DNNs. Existing adversarial training methods are facing the challenge to balance the accuracy and robustness.

Deep neural networks (DNNs) are demonstrated to be vulnerable to adversarial examples. Adversarial training is mainstrem method to improve adversarial robustness of DNNs, which augments the training set with adversarial examples and adopts adversarial regularization loss to improve the robustness of DNNs. Existing adversarial training methods are facing the challenge to balance the accuracy and robustness.

Understanding the dynamics of cyber threats is crucial for today's digital defenses. Researchers from Nanjing University, Jiangsu University of Science and Technology, and Southeast University have discovered that optimizing the execution time of malware in sandbox environments can significantly enhance the completeness and quality of cyber threat intelligence (CTI) data.

A new Children’s Hospital of Philadelphia study examines the relationship between parenting factors and gaming disorder in young children with ADHD. Findings from the study will be presented during the Pediatric Academic Societies (PAS) 2026 Meeting, taking place April 24-27 in Boston.