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Paper Citation Record · LEDGER

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval

As of 7 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2509.07532.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.07532 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:06:39.538583Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

  • verified exact0
  • verified fuzzy46
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65485d3e-1d1f-4fc0-9f41-d28aba9b7feb · outbound

This paper cites Continuous learning for Android malware detection.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Continuous learning for Android malware detection

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 48d36bd3-5368-45bb-94de-abc02152fa8a · outbound

This paper cites Automated, reliable zero- day malware detection based on autoencoding architecture.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Automated, reliable zero- day malware detection based on autoencoding architecture

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.211620Z digest=sha256:56ce292f3ffbe95ee2c01670ed4ba1b34a99b303f09b00966ff7f262f645e8f7

Observation 85dd0669-cf12-4f40-bdc3-ce774f8d44ef · outbound

This paper cites ArchSentry: Enhanced Android Malware Detection via Hierarchical Semantic Ex- traction.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval ArchSentry: Enhanced Android Malware Detection via Hierarchical Semantic Ex- traction

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 204dad11-23dc-4375-8c9c-c1b0cb331373 · outbound

This paper cites Entropy-based sample selection for online contin- ual learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Entropy-based sample selection for online contin- ual learning

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.308290Z digest=sha256:e4123bc14e297ecf867ace4608fa7fdf3728452b4f79127ee1c846e879cb05f5

Observation cb2b77c7-a66e-4b5b-8306-cde4abb9abfe · outbound

This paper cites Active learning literature survey.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Active learning literature survey

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:06:38.382611Z digest=sha256:53c7e8f3ebb66cc342d531dcd874926bc19a851bc812af6506a72d739a331652

Observation 2d7c42df-4ede-4f99-ba69-8c51acaa1554 · outbound

This paper cites Uncertainty in deep learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Uncertainty in deep learning

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.420023Z digest=sha256:a69be5e62f3c2290303a834a6c9255c231076e1aafefb2442d45f48375ffee0b

Observation c900b9d8-8d4d-4e1b-9699-a77478db2006 · outbound

This paper cites The power of ensembles for active learning in image classification.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval The power of ensembles for active learning in image classification

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.496259Z digest=sha256:416256132ce88143315af86cfa3ee478aefbd2c02c36c141879b4384600b5843

Observation ffc0dbfe-9847-4430-b893-11a570b6e709 · outbound

This paper cites Discriminative Active Learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Discriminative Active Learning

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:06:38.570181Z digest=sha256:b7ab454a7c87190cadcfccc8aaec81b2d5d6b19a15dc8ae0433cf38f5b5ee2fb

Observation eb590e13-5845-4a73-94d7-fd51be0490a4 · outbound

This paper cites Semi-supervised learning with variational Bayesian inference and maximum uncertainty regularization.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Semi-supervised learning with variational Bayesian inference and maximum uncertainty regularization

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.587018Z digest=sha256:64823dac0a53d669e975efb99a6cc1100d8dc5f755fca798e11fde1f06016f8d

Observation 742eca19-b87c-413b-a77b-ad2a6ece49e7 · outbound

This paper cites Uncertainty-based continual learning with adaptive regularization.Advances in Neural Information Processing Systems, 2019, 32.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Uncertainty-based continual learning with adaptive regularization.Advances in Neural Information Processing Systems, 2019, 32

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.595515Z digest=sha256:f112fd08b4ecd0e80b1d6b1da98b1e67f95004f9d8f2841728309a915f0f4468

Observation e0877796-7ae7-4c5d-8a94-9b8273b92e21 · outbound

This paper cites Transcending TRANSCEND: Revisiting malware classification in the presence of concept drift.2022 IEEE Symposium on Security and Privacy (SP), 2022: 805-823.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Transcending TRANSCEND: Revisiting malware classification in the presence of concept drift.2022 IEEE Symposium on Security and Privacy (SP), 2022: 805-823

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.691891Z digest=sha256:bd423bae9d86026a6345f1408c95e86021abf2eda61f69863f32c3e75f83a736

Observation 413d4d20-930e-46fb-ad2d-e90a79ba0a15 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the National Academy of Sciences, 2017, 114(13): 3521-3526.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Overcoming catastrophic forgetting in neural networks.Proceedings of the National Academy of Sciences, 2017, 114(13): 3521-3526

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:38.798973Z digest=sha256:4a2f399630765887bfb90778be92ee7ac932bf4f6fdb7b50241f8fb0c43fd868

Observation f5e0c58c-fb4b-4303-a2a0-248ad3c2bea2 · outbound

This paper cites Progressive Neural Networks.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Progressive Neural Networks

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:06:38.903708Z digest=sha256:bfc9de307355ddb91d527f2305b7e919bf1b4a8dc92ca9200153b5edfbe5d4d2

Observation ccdcd802-84af-4513-9773-98161d7a5064 · outbound

This paper cites iCaRL: Incremental classifier and representation learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval iCaRL: Incremental classifier and representation learning

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.013540Z digest=sha256:15f3e5e5884650b474ece33418bc6dedd7dc1e2db05e0cd33004054ffddea237

Observation 32835fa0-177e-435a-95eb-05bb513983c2 · outbound

This paper cites DER: Dynamically expandable representation for class incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval DER: Dynamically expandable representation for class incremental learning

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.057027Z digest=sha256:966fb74e4cd874ceacab332229b86603e3c6e53ff13eefd4d4d9389d255ec0ad

Observation 60e8605c-32aa-4a8d-93fe-3d07a5dc09fe · outbound

This paper cites PODNet: Pooled outputs distil- lation for small-tasks incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval PODNet: Pooled outputs distil- lation for small-tasks incremental learning

Reference 16

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.167200Z digest=sha256:b992c84cbcfd41aa9ec1f93d4c27b38ec81dddbf55d1f02600d26f776de69b40

Observation 06f0204c-29af-4f33-8e51-d66a01807648 · outbound

This paper cites Toward deep super- vised anomaly detection: Reinforcement learning from partially labeled anomaly data.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Toward deep super- vised anomaly detection: Reinforcement learning from partially labeled anomaly data

Reference 17

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.227120Z digest=sha256:2193309572d8093ee7a7dd83dfc1974e4c41c163ca772837915e385761c587ca

Observation 21daebbd-7f8c-40a9-9ba9-038967cdbfd8 · outbound

This paper cites Towards Building Generalizable Models for Malware Detection.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Towards Building Generalizable Models for Malware Detection

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.281175Z digest=sha256:77f23cccde2edf976d439957e2c50298d4fef97c770ec42bb6f4c6611d18228a

Observation 1d527f45-13a0-4ede-a457-e558c31a630b · outbound

This paper cites Improving adversarial robustness using knowledge distillation guided by attention information bottleneck.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Improving adversarial robustness using knowledge distillation guided by attention information bottleneck

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.389230Z digest=sha256:03521596e9f51e29fac4917010d7cf315594365f5f71f128f66f28d9ca16e3a9

Observation affea943-b7cc-4dd0-831f-b10e4c2e4bd1 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Model-agnostic meta-learning for fast adaptation of deep networks

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.406854Z digest=sha256:c750433b2560739d792cc6d22b2d79fc4f5c5bc1dcf9b32e7319041e569d9600

Observation cf1a4f74-63e0-4b19-8b5f-fdd01547d1fd · outbound

This paper cites Prototypical networks for few-shot learning.Advances in Neural Information Processing Systems, 2017, 30.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Prototypical networks for few-shot learning.Advances in Neural Information Processing Systems, 2017, 30

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.435170Z digest=sha256:8c7994edb559e58c4ef119b8141fe31987e829fb6562cf23edbea83444d63da6

Observation e5c96f80-80b8-4534-af59-89712712f88e · outbound

This paper cites Learning to compare: Relation network for few-shot learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Learning to compare: Relation network for few-shot learning

Reference 22

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.471478Z digest=sha256:eeb062bfefe9789075ba7e514647fcb45cb6dcf08fd1f08169afcb2656e892f7

Observation 56f9aab7-8ee6-4451-8e7f-2dfb12cb267d · outbound

This paper cites Meta-baseline: Exploring simple meta- learning for few-shot learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Meta-baseline: Exploring simple meta- learning for few-shot learning

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.473742Z digest=sha256:c806f71d55b107f780d8ed74f5976352efd725564f7a127749b17719008413dd

Observation 79a75e0c-03e6-4621-84e7-6af0b050ad8f · outbound

This paper cites A Baseline for Few-Shot Image Classification.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval A Baseline for Few-Shot Image Classification

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:06:39.476046Z digest=sha256:d934438038472ae2a93506bde67becc50beb518682dbeb4b381bbfd51caf0cd7

Observation 288fc271-0d42-4656-ba26-84b9f9f8f7d0 · outbound

This paper cites Meta-learning for multi-family android malware classification.ACM Transactions on Software Engineering and Methodology, 2024, 33(7): 1-27.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Meta-learning for multi-family android malware classification.ACM Transactions on Software Engineering and Methodology, 2024, 33(7): 1-27

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.478853Z digest=sha256:9bfc5188695a52d2e0853f7fa67e0ef6cbc438938008e7c9412fac7ae19474fe

Observation 49d1d912-c833-4e88-af10-8c1b3f450e64 · outbound

This paper cites NF-GNN: Network flow graph neural networks for malware detection and classification.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval NF-GNN: Network flow graph neural networks for malware detection and classification

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.481057Z digest=sha256:d7bb80be7d7b83c082f779adddf7c99bfe5417126aa7f6f370096c263e9470c1

Observation a6db91a7-f3c5-43b0-89a7-64f85ca1f4e3 · outbound

This paper cites FewM-HGCL: Few-shot malware variants de- tection via heterogeneous graph contrastive learning.IEEE Transactions on Dependable and Secure Computing, 2022.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval FewM-HGCL: Few-shot malware variants de- tection via heterogeneous graph contrastive learning.IEEE Transactions on Dependable and Secure Computing, 2022

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.483426Z digest=sha256:d50bf2765f567c7cb5bef10ca98e593f0b12ddf632d863c7f12d40197853df61

Observation 45c5ce1e-de92-403a-a0ff-d5ec1c10a814 · outbound

This paper cites Few-shot class-incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Few-shot class-incremental learning

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.485815Z digest=sha256:60300c4928dcb7e00357e84e1b91b5bac493ee395c5bf617e5522a34e3c01942

Observation 3930ed5b-27a9-4c75-833a-19a55995c576 · outbound

This paper cites Incremental few-shot learning with attention attractor networks.Adv.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Incremental few-shot learning with attention attractor networks.Adv

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.488415Z digest=sha256:38b56a3a599b56c9a33965895b9ce6780be6187026f27985746810af9b9e12a0

Observation 994aa342-35d5-4c94-bdef-7aafbb3ba249 · outbound

This paper cites Few-shot lifelong learning.Proc.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Few-shot lifelong learning.Proc

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.490786Z digest=sha256:b891f81c2cbe48a2df9a515ab76d6f9f919e5994d21f9bb28bc6b8cef2782347

Observation 9db8edd3-1e37-4880-8b6a-cb6a8340803b · outbound

This paper cites Self-promoted prototype refinement for few-shot class-incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Self-promoted prototype refinement for few-shot class-incremental learning

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.492889Z digest=sha256:873a8d1fc667e2dc90310ef6451bec945980981ec2c730c8a4b6ba1a4780af90

Observation 850e4499-125a-43d9-a2d8-3bf949872873 · outbound

This paper cites Semantic-aware knowledge distillation for few-shot class incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Semantic-aware knowledge distillation for few-shot class incremental learning

Reference 32

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raw_fallback, observed 2026-08-04T22:06:39.739887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.494993Z digest=sha256:1c9c7360a5cf624fff209f2d26524693058df079d1893f52969692ecb93fbbb1

Observation 305b35a8-daae-4894-9080-d0cadd3592ee · outbound

This paper cites An incremental malware classification approach based on few-shot learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval An incremental malware classification approach based on few-shot learning

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.732624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.497185Z digest=sha256:20902b6cf0b4b6726b6196a01cd2aabdcb63a4f26a78ac40641c74761bb211b9

Observation ce25736c-4a64-4522-b79b-0f2fe241b173 · outbound

This paper cites Forward compatible few-shot class- incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Forward compatible few-shot class- incremental learning

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.725271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.499465Z digest=sha256:4662aed136fa66278f5a1c25114c8b9abb441740f4d86cd23f1175a4833e61e6

Observation ad804256-8f13-4310-929e-f4be42e06a7d · outbound

This paper cites Few-shot class-incremental learning via relation knowledge distillation.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Few-shot class-incremental learning via relation knowledge distillation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.718074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.501860Z digest=sha256:a82182aa590a97cff8f7a289e33b659dcadec1df83f05cd21a542c156cbddca0

Observation 41def54b-fac8-412d-9685-7ad790cca3ab · outbound

This paper cites GPTree: A Gaussian process classifier for few-shot incremental learning.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval GPTree: A Gaussian process classifier for few-shot incremental learning

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.710761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.504168Z digest=sha256:5ac2f5aac77cbf873ea3b13396923254c52ea21a8d57868dc7e5dbe6f9462f44

Observation c022fb4d-12f0-4c5a-80af-87843615a87c · outbound

This paper cites Few-shot class-incremental learning via compact and separable features for fine-grained vehicle recognition.IEEE Trans.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Few-shot class-incremental learning via compact and separable features for fine-grained vehicle recognition.IEEE Trans

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.703253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.506365Z digest=sha256:1928d736e990ba999c5050436a08a56afafdd4210a7d833b447cdcdd9f2bbb2e

Observation dc2498ec-6c90-425a-9927-68f05bd79408 · outbound

This paper cites Evidential deep learning to quantify classification uncertainty.Advances in Neural Information Processing Systems, 2018, 31.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Evidential deep learning to quantify classification uncertainty.Advances in Neural Information Processing Systems, 2018, 31

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.695019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.508777Z digest=sha256:92c613ad48dcd814a93f686fbe867a726bd0415ae86b688e5ec72f0ea5cfb3f4

Observation a5b3922f-753b-4316-8eb9-97eb227f8cb2 · outbound

This paper cites In:Computers in Biology and Medicine.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval In:Computers in Biology and Medicine

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.687110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.511456Z digest=sha256:50f970f2c3e8eecef146e65be1e0c0ab6e346705d939c37b99e9b80aa0e396d2

Observation a2c0a772-8656-4d11-b60e-72ab52e0b16b · outbound

This paper cites EVIL: Evidential inference learning for trustworthy semi-supervised medical image seg- mentation.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval EVIL: Evidential inference learning for trustworthy semi-supervised medical image seg- mentation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.679352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.513696Z digest=sha256:0089de09c94d284a45dabf2adf63866f546e3475464ee846475231a83b8cc43d

Observation 704e235d-f402-4b40-8098-c491f0938b38 · outbound

This paper cites Patient-level anatomy meets scanning-level physics: Personalized federated low-dose ct denoising empowered by large language model.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Patient-level anatomy meets scanning-level physics: Personalized federated low-dose ct denoising empowered by large language model

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.671622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.516150Z digest=sha256:77d06dbdf654f82df7e008adb8d070a31497350c0cb26f1aef6a189899cf8fe3

Observation 0f0ce0e3-2740-4848-97b6-e8f67856f3d7 · outbound

This paper cites Enhancing state-of-the-art classifiers with API semantics to detect evolved Android malware.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Enhancing state-of-the-art classifiers with API semantics to detect evolved Android malware

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.663958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.518785Z digest=sha256:c5c71ac739deb469257e613e79f2605f621b738c4b8f2c3e70e7fdb40c5fccc4

Observation 02b92ddf-d0c7-45cf-88ac-1b9135cc4be8 · outbound

This paper cites https://androzoo.uni.lu/.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval https://androzoo.uni.lu/

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.655995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.521250Z digest=sha256:9465301496621b2cfe95b228c94981dc78151e91140183703fd6a97b90e1706e

Observation fcaa7663-173b-4e0e-b92c-75b3b124aca9 · outbound

This paper cites https://www.virustotal.com/.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval https://www.virustotal.com/

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.648400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.524190Z digest=sha256:0303843ce2001cd03f4d92cb9c628ca2d7ece40c62a0d56b774df8c3c3376e67

Observation c723c9c8-5e65-46f2-910a-58c7ae8313f5 · outbound

This paper cites https://virusshare.com/.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval https://virusshare.com/

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.640597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.526554Z digest=sha256:b02926bb20658af1b7f03e60cd4e07e362436d26b9a33815f7e459a23d01df59

Observation 0598815e-104e-47bb-8007-f8cb4c3964ff · outbound

This paper cites Dos and don’ts of machine learning in computer security.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Dos and don’ts of machine learning in computer security

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.632144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.529048Z digest=sha256:fbf73b42469c684c52244b9ed148606763c4501b8d79d00a634be5c5904fb506

Observation 311e4d75-2058-4fa2-8b43-5c3440f9cfa9 · outbound

This paper cites AndroZoo: Collecting millions of Android apps for the research community.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval AndroZoo: Collecting millions of Android apps for the research community

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.617155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.531564Z digest=sha256:c0dcd8bc6ec28fecca6055cdd34089a9be5f0e5a9dc16d5ec5093bd9fb110a45

Observation 9f9bb037-dc0f-403b-89f9-5531c0bca6fd · outbound

This paper cites Deep ground truth analysis of current Android malware.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval Deep ground truth analysis of current Android malware

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.604266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.533905Z digest=sha256:733e2e895d1f498addf8f9d0e17d70637e0d02b17bb1620927692aea5492e4cf

Observation 67a6abad-9e8f-493f-a61c-e49abe9f9511 · outbound

This paper cites BODMAS: An open dataset for learning based temporal analysis of PE malware.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval BODMAS: An open dataset for learning based temporal analysis of PE malware

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.596333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.536210Z digest=sha256:396eab7f40937e69989502b61ff48a7b4737f42835a1f4ba0921f0c9a9c59dc9

Observation 36a323df-94a5-451f-81f6-037e58cad773 · outbound

This paper cites CADE: Detecting and explaining concept drift samples for security applications.

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval CADE: Detecting and explaining concept drift samples for security applications

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:06:39.586265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-04T22:06:39.538583Z digest=sha256:a0d6459c0abf9b5d7c1d4bc291b2a0646068e1297888a25256c3d061021e0ab7

Pith citing papers

No inbound Pith citation observations are available.