Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition

arXiv:2401.10337 · cs.LG, cs.AI, cs.CL, cs.CR · Submitted 2024-01-18 · Read on arXiv

cs.LG, cs.AI, cs.CL, cs.CR

Submitted: 2024-01-18

Updated: 2026-08-26

Code: https://github.com/CTID/TRAM

Terminology

• Transformer — 17× in this paper · explained in 2 episodes
• Actor — 11× in this paper · explained in 3 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Noise Contrastive Estimation — 4× in this paper
• Decode — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Cyber Threat Intelligence — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Ontology — 2× in this paper · explained in 2 episodes
• Probabilistic Label Trees — 2× in this paper
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Personally Identifiable Information — 1× in this paper
• Regret — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Extreme Classification via Adversarial Softmax Approximation
• Probabilistic Label Trees for Extreme Multi-label Classification
• Representation Learning with Contrastive Predictive Coding
• Deep Learning is Robust to Massive Label Noise

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