Only 80 of 250 ATT&CK techniques (32%) allow plausible decoy placement by defenders, grouped into Sweep and Seek patterns, with the rest having no suitable defender asset in the attack path.
A sampling-neighborhood- regularized latent factorization of tensor for dynamic qos estimation,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Benchmark study finds calibrated rule-based controller outperforms six DRL algorithms on cost for adaptive resource control across workloads, with action-space mismatch explaining large differences in constraint violations.
scKDGM proposes a KAN-guided dynamic graph masked learning framework with GDP-Mask, TAKGCN encoder, mask-guided recovery, cross-view contrastive learning and ZINB loss that outperforms 10 baselines on 12 scRNA-seq datasets in NMI and ARI.
citing papers explorer
-
Decoys Cannot Go Everywhere: Mapping the Deception Surface in MITRE ATT&CK
Only 80 of 250 ATT&CK techniques (32%) allow plausible decoy placement by defenders, grouped into Sweep and Seek patterns, with the rest having no suitable defender asset in the attack path.
-
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
Benchmark study finds calibrated rule-based controller outperforms six DRL algorithms on cost for adaptive resource control across workloads, with action-space mismatch explaining large differences in constraint violations.
-
scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering
scKDGM proposes a KAN-guided dynamic graph masked learning framework with GDP-Mask, TAKGCN encoder, mask-guided recovery, cross-view contrastive learning and ZINB loss that outperforms 10 baselines on 12 scRNA-seq datasets in NMI and ARI.