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Found 3 papers, 1 papers with code
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization
Extensive experiments show that RoMA significantly outperforms seven competing methods in both adversarial robustness (e. g., achieving over 80% robust accuracy-more than twice that of the next-best method under PGD attacks) and training efficiency (e. g., more than twice as fast as the second-best method in terms of accuracy), while maintaining superior standard accuracy in non-adversarial scenarios.
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type.
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS).
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