FreeMOCA

Trajectoire d'apprentissage continu comme mémoire pour une consolidation sans rejeu dans l'analyse de code malveillant !NOTE Ceci est l'implémentation officielle de l'article Continual Learning Trajectory as Memory for Replay-Free Consolidation in Malicious Code Analysis. Pipeline FreeMOCA FreeMO...

No Free Efficiency: Revisiting the Trade-Off between Training Efficiency and Model Vulnerability

Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements of large-scale training, researchers increasingly adopt strategies such as selective data sampling, efficient pre-training, and simplified...

Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

Network intrusion detection systems NIDS are critical for cybersecurity, safeguarding services and data from potential attacks. However, existing AI-based NIDS often assume static data distributions and fail to handle concept drift, leading to degraded performance and increased false positives in...

Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition

The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forgetting, where previously learned knowledge is overwritten. While continual...

Quantifying Catastrophic Forgetting in IoT Intrusion Detection Systems

Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale connected systems. Intrusion Detection Systems IDS trained on static datasets often fail to generalize to unseen threats and suffer from catastrophic forgetti...