DUPIN: Attack Learning Is Still Needed! Demonstrating Few-Shot after Unsupervised Pretraining Is A Nimble Forensics Learner

arXiv:2609.00259 · cs.CR · Submitted 2026-08-31 · Read on arXiv

cs.CR

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/darpa-i2o/Transparent-Computing

Project page: https://pytorch.org

Terminology

• Trace — 52× in this paper · explained in 2 episodes
• Provenance — 38× in this paper · explained in 4 episodes
• AUROC — 14× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Embeddings — 13× in this paper · explained in 2 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Few-Shot Learning — 8× in this paper · explained in 4 episodes
• Self-Supervised Learning — 6× in this paper · explained in 8 episodes
• Actor — 5× in this paper · explained in 3 episodes
• Attention Mechanism — 5× in this paper · explained in 3 episodes
• Security and Privacy — 5× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Graph Construction — 4× in this paper · explained in 3 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Darpa Transparent Computing — 3× in this paper
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• GNNs — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
• Semi-Supervised Classification with Graph Convolutional Networks
• graph2vec: Learning Distributed Representations of Graphs
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Graph Attention Networks
• How Powerful are Graph Neural Networks?

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