{"as_of":"2026-08-21T11:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f8179f890bd975ff2673b5923f7857603b65f6e2d83cfc0699aa8bcb0838440e","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:39:39.168929Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.06750/citation-record","integrity":"/paper/1908.06750/integrity","json":"/paper/1908.06750/citation-record.json","paper":"/paper/1908.06750"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.879581Z","title":"Classiﬁcation of ransomware families with machine learning based on N-gram of opcodes,","venue":null,"work_id":"155ac7f5-0fff-4be8-8f87-06e2f273adaa","year":2019},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.946716Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:d1028e252eb797081685f05899b3b28090d2eaf005744858a38ac8a9448db5b8","observation_id":"b968fa4c-9aa1-4e60-99d6-fb788ce30133","resolution":{"observed_at":"2026-08-14T12:39:39.884131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.865913Z","title":"Ransomware, threat and detection techniques: A review,","venue":null,"work_id":"06c18831-e81a-4409-a545-ccf390074765","year":2019},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.952145Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:1640ee6fc99891ef934d0227c8c487bac2f113a56f0828ef38dc6746363dbef1","observation_id":"1b72a6cd-8302-42bf-a155-85c0516e6c39","resolution":{"observed_at":"2026-08-14T12:39:39.870153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.851738Z","title":"Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions,","venue":null,"work_id":"9dd12fb6-f47b-4051-af39-6f0afb1f97df","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.956473Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:b535655d202bb9dfcf4d7657240ca1468ce2bfdc4a0c4aeb2e29a68ca1c45028","observation_id":"d009690c-2bcf-429a-b5ca-00f01bc004f0","resolution":{"observed_at":"2026-08-14T12:39:39.856637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.837781Z","title":"Evaluating shallow and deep networks for ransomware detection and classiﬁcation,","venue":null,"work_id":"b9f4b1fe-0442-41c0-ac17-26556877bddf","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.962039Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:77e8b0b51304ebde44fe74ff859123238325105a06674dc1d058be4198b8c93d","observation_id":"b49d9f7a-2f5c-43c1-953c-e811994d364a","resolution":{"observed_at":"2026-08-14T12:39:39.842323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.823791Z","title":"Extinguishing ransomware - A hybrid approach to Android ransomware detection,","venue":null,"work_id":"ac5161e9-05dd-46fd-b8a1-b3d40702205a","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.966530Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:5933e7c75b5c965b42a396b4ba5f9820ce0fdf527245c474686f24d08d9af704","observation_id":"bd8eee9c-184b-4c72-8e43-3b159aca196e","resolution":{"observed_at":"2026-08-14T12:39:39.828580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-17T19:17:06.411141Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-14T12:39:38.971316Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.971316Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:5dc4c5c2c288df87fa9028237fa220f8f9066d15eb2a032d367a55e190eae590","observation_id":"69fcd322-bbbf-4ffd-a909-67fba3e95ca2","resolution":{"observed_at":"2026-08-14T12:39:38.971316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.809792Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"f76eafb6-c1b2-4d69-9ec4-3391936ed57a","year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.976965Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:625d9d631941caf0c910cb63b35d3050d258e6da510ed93e155ca86b09346653","observation_id":"10fb691f-57a9-409d-8b3a-ef72b3d286e3","resolution":{"observed_at":"2026-08-14T12:39:39.814414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.07360","last_updated":"2016-11-04T21:26:08Z","snapshot_observed_at":"2026-08-14T22:09:04.541956Z","submitted_at":"2016-02-24T00:09:45Z","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.07360","snapshot_observed_at":"2026-08-14T12:39:38.980604Z","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.980604Z"},"links":{"cited_paper":"/paper/1602.07360","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:5e786252fd6dce6a588052e908b99615b79aafdaa16a00ce8bac7eb5032cf481","observation_id":"93485287-1f10-479c-8ab3-ed2ae8201fb1","resolution":{"observed_at":"2026-08-14T12:39:38.980604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.796062Z","title":"Densely connected convolutional networks,","venue":null,"work_id":"37bd3699-2c50-4191-bf1f-ea66e47c5261","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.984734Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:3361cbc21776310f5cfd7574b3d546dee4f594e35a3a8a5c06e0eea55f9641c3","observation_id":"7bcf0d67-cd55-4e36-8b1b-1b2ad379840f","resolution":{"observed_at":"2026-08-14T12:39:39.800696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.782131Z","title":"Rethinking the Inception architecture for computer vision,","venue":null,"work_id":"8dc570d7-1bba-4f4b-b9cb-e86fdaba6aff","year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.989852Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:7c8560be968ce80f304c4b3b0d944f572c3502348a375bfdeaebed0703038c47","observation_id":"dcdf9226-2646-44bc-a1f0-d810c3a4a3ce","resolution":{"observed_at":"2026-08-14T12:39:39.786540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.768812Z","title":"ShufﬂeNet v2: Practical guidelines for efﬁcient CNN architecture design,","venue":null,"work_id":"d5b8ee78-e6b4-491c-a723-98775d7b8c7c","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.994328Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:d5a8caf31d1cb0f4fc9b0348bd061ddb97bd9e10a79911d8e21b289e9aadf249","observation_id":"33b28ebf-740b-4c05-ab07-094af9510064","resolution":{"observed_at":"2026-08-14T12:39:39.773440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.755080Z","title":"MobileNetv2: Inverted residuals and linear bottlenecks,","venue":null,"work_id":"0731cba4-e806-4d0f-a0fe-44807ce66fd1","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:38.999433Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:fc8469b1aebcbf2962f3cf746877ea4f68e817b244842eb4a25efc0167a9d284","observation_id":"5b4388ed-17d4-46cc-bc51-e90ead5f994a","resolution":{"observed_at":"2026-08-14T12:39:39.759624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.742120Z","title":"Aggregated residual transformations for deep neural networks,","venue":null,"work_id":"e94827f3-ffe4-4cf6-a872-d11d1cdc731f","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.003311Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:19bacdfe8b93c3dd8cb32afaaa2f2987f6792f6dc61effadd246c1ef45fe703b","observation_id":"68323498-ccf1-4aed-b195-9f056b73fdd8","resolution":{"observed_at":"2026-08-14T12:39:39.746385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.728792Z","title":"How to make a neural network say dont know,","venue":null,"work_id":"99e35993-7bb9-4b03-9712-f0698ff2e06e","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.008118Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:973f5ef8229dc1b0686b77959810690ce6d7db76ae9dc8da4d9a068b4c165e0d","observation_id":"f2ce3def-66aa-47bb-aa04-6c7e6ca3ae0c","resolution":{"observed_at":"2026-08-14T12:39:39.733620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.714896Z","title":"Adversarial examples in the physical world,","venue":null,"work_id":"4dae64c6-57fb-463e-833e-3f54671cbbc6","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.012257Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:c57dcb28424fe1c845dd23392b7f55618250c17e69f15a6bb100acc7a3719a01","observation_id":"e23ab249-6b8b-42cf-a9fb-a3a6071de66b","resolution":{"observed_at":"2026-08-14T12:39:39.719650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.702182Z","title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,","venue":null,"work_id":"5ca3f387-5dfb-48e4-8d06-9fb472e5de37","year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.016357Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:eef297f86a33a6fb75314e14b07bcabd110afa85b5020d4bcc4858008ce76068","observation_id":"b42d9889-007a-4c58-856b-c0559f244a40","resolution":{"observed_at":"2026-08-14T12:39:39.706624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.689633Z","title":"Concrete dropout,","venue":null,"work_id":"0c8646b9-0e81-46a5-bb72-0e9872f32146","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.021445Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:3246fc81445e674330ab3d9abcdfa5cb4d95bdbe529e0b6365bb84910599c4e4","observation_id":"ab3c49e0-443e-42c8-8d6f-6a62d4c0a955","resolution":{"observed_at":"2026-08-14T12:39:39.693969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.676861Z","title":"Variational dropout and the local reparameterization trick,","venue":null,"work_id":"ef4baa47-739b-4a0d-947b-09b119ef6b25","year":2015},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.025806Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:23ae0e6dc5129ab562ea3c3755c3fc5b14f4e55572f937a55a707100bf07a3be","observation_id":"bfbb39af-fc48-4b7f-8992-6bad3c1d6a9a","resolution":{"observed_at":"2026-08-14T12:39:39.681127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.02989","last_updated":"2017-11-08T15:04:20Z","snapshot_observed_at":"2026-08-14T20:15:40.926984Z","submitted_at":"2017-11-08T15:04:20Z","title":"Variational Gaussian Dropout is not Bayesian","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.02989","snapshot_observed_at":"2026-08-14T12:39:39.031360Z","title":"Variational Gaussian dropout is not Bayesian,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.031360Z"},"links":{"cited_paper":"/paper/1711.02989","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:78ef9060b52616e89e034c74d618b848b5f6d21b69a00163fb6a30394948cb24","observation_id":"6a99925e-2b97-4d1f-a1ea-c8c2caf92ec8","resolution":{"observed_at":"2026-08-14T12:39:39.031360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01969","last_updated":"2018-07-05T12:48:53Z","snapshot_observed_at":"2026-08-14T18:55:24.322094Z","submitted_at":"2018-07-05T12:48:53Z","title":"Variational Bayesian dropout: pitfalls and fixes","version":1},"cited_work":{"arxiv_id":"1807.01969","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.01969","snapshot_observed_at":"2026-08-14T12:39:39.249261Z","title":"Variational Bayesian dropout: pitfalls and fixes","venue":"stat.ML","work_id":"f20b4257-9db4-4412-bb49-2f9fa91fc267","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.036514Z"},"links":{"cited_paper":"/paper/1807.01969","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:74930bbf57eee7e0d933b96e2918b642f92bbe0ad3f12555dd573c3010303aab","observation_id":"d3fc5942-a056-4e03-a73f-c25bf4745ee1","resolution":{"observed_at":"2026-08-14T12:39:39.254664Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.664426Z","title":"Botminer: Clustering analysis of network trafﬁc for protocol and structure independent Botnet detection,","venue":null,"work_id":"d7c4dd17-3e8b-4067-8476-3e99bee24fea","year":2008},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.040663Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:a239cb0b5e84b5aa0cf33b03cee2fee26ea5d346aedc079d6347231fa2c86c78","observation_id":"2e92bf12-2cf2-4f6c-9faf-eaa739a51e13","resolution":{"observed_at":"2026-08-14T12:39:39.668687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.652101Z","title":"Software-deﬁned networking-based ransomware detection using HTTP trafﬁc characteris- tics,","venue":null,"work_id":"0cac0bf8-078b-428d-987f-a7c904a0599b","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.045626Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:d3c195b5ca2dbfab228c14d008fc139d305ac987f473eceb472cabcb8ab5685b","observation_id":"ffbf1448-c189-43fc-931e-de79ee81d71f","resolution":{"observed_at":"2026-08-14T12:39:39.656154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.639167Z","title":"Scalable, behavior-based malware clustering,","venue":null,"work_id":"31cb4a68-6fa7-4e09-b0e3-6ab35faf2397","year":2009},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.049545Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:26fad680557398759896b55e7f46f4b4b7db655e15793d5f3551c86bf3762e9e","observation_id":"3d2b04f3-dd70-4a4d-8473-44b7281e53af","resolution":{"observed_at":"2026-08-14T12:39:39.643178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.623935Z","title":"JACKSTRAWS: Picking command and control connections from Bot trafﬁc,","venue":null,"work_id":"4d9e531b-f818-479f-8de5-a1f5ad562364","year":2011},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.053445Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:d020437b3f3bd5c31ced51cb36a125b1056be0fa0891afdbe23a659fd734c3b7","observation_id":"1f39472a-891b-4991-8998-962ae0fc07a1","resolution":{"observed_at":"2026-08-14T12:39:39.629250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.610195Z","title":"Heldroid: Fast and efﬁcient linguistic-based ransomware detection,","venue":null,"work_id":"555fcc74-b802-4b76-a5dd-c890138a40bd","year":2015},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.057470Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:42881f0510988ca4ffd21b0b4bed5796e4b576c41a396e2acfa61331072d0916","observation_id":"61c63d31-3ef2-4f2e-85d8-c8b1d37e6052","resolution":{"observed_at":"2026-08-14T12:39:39.614329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.595241Z","title":"Cutting the gordian knot: A look under the hood of ransomware attacks,","venue":null,"work_id":"0163632c-4bbb-4291-af37-bc5cc1edea60","year":2015},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.062005Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:27474e9599820e4dde9d55a5d55f01323d7e88565c23001398a24d6d5e63037a","observation_id":"04046f35-f5f7-4987-8265-eecbcec21e05","resolution":{"observed_at":"2026-08-14T12:39:39.600086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.581223Z","title":"Cryptolock (and drop it): Stopping ransomware attacks on user data,","venue":null,"work_id":"e7f181b9-a516-4505-b695-55810923ced0","year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.066509Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:db23a8e3dd9b9c672e1b1c516253d777a63d0b653d4abab39636a5b5ca30df11","observation_id":"107de4d6-abfc-4c66-a840-6ec8f79adda3","resolution":{"observed_at":"2026-08-14T12:39:39.586301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.03020","last_updated":"2016-09-10T09:49:36Z","snapshot_observed_at":"2026-08-14T21:40:29.001190Z","submitted_at":"2016-09-10T09:49:36Z","title":"Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.03020","snapshot_observed_at":"2026-08-14T12:39:39.070219Z","title":"Au- tomated dynamic analysis of ransomware: Beneﬁts, limitations and use for detection,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.070219Z"},"links":{"cited_paper":"/paper/1609.03020","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:87e03178e35125ba2424a9cc6826b643cb8c2fede3b5104aa9de3dd15e342532","observation_id":"5cfeedbe-efb3-4198-a781-952e293de4e9","resolution":{"observed_at":"2026-08-14T12:39:39.070219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.567986Z","title":"Evaluating shallow and deep networks for ransomware detection and classiﬁcation,","venue":null,"work_id":"f44ba452-9bc4-4b24-9357-b5681583db58","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.074359Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:1b5f8825d981d0c7b1fa154ffd9259ce5c71066b4aa2229d99708e647f0cedac","observation_id":"f0b98535-e43d-4cd6-a22c-018bc1fe31ea","resolution":{"observed_at":"2026-08-14T12:39:39.572335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.553177Z","title":"Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer,","venue":null,"work_id":"3bbf6f50-7ae0-4993-b528-38afbadebaf2","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.078221Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:fa61ca52eec51d9d6a310764ffff81984c1163f095df7c8f538c62b7e93c4ad7","observation_id":"66d2c3f0-b2db-4cd8-b0c0-60e97fa441de","resolution":{"observed_at":"2026-08-14T12:39:39.558664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.081986Z","title":"Faster R-CNN: Towards real- time object detection with region proposal networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.081986Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:480fbf48e5c58fe86fc9736847d94972bf489d6199461df3d9c09ca7176ca4e4","observation_id":"aa6485b7-dc6a-4c5c-9e2f-b91878b5c34f","resolution":{"observed_at":"2026-08-14T12:39:39.081986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.530397Z","title":"Veritatem Dies Aperit - temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach,","venue":null,"work_id":"b7fc1cac-80db-4e4a-bdf2-77f9bbca6a5d","year":2019},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.085878Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:61187f0415835e7571d83a1b2cac27bd32368d2bdd68fa431537313090219caf","observation_id":"c60180a3-f1fb-4152-90f7-e4cf6dcd6ac9","resolution":{"observed_at":"2026-08-14T12:39:39.535413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.517069Z","title":"Distributed representations of words and phrases and their composi- tionality,","venue":null,"work_id":"a5ef2d0a-eeb9-4917-ac09-1a6b2cbb70d9","year":2013},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.089760Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:a527bae22d290c7a4a5779e67ab0ff6fc9f9feda5a472a33653a93d80e5d9265","observation_id":"b8848b5e-b7f5-449b-839f-438ddf1fd080","resolution":{"observed_at":"2026-08-14T12:39:39.521417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.504059Z","title":"node2vec: Scalable feature learning for networks,","venue":null,"work_id":"aa1b4fca-ca09-43f6-bf81-30fabf226591","year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.093670Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:b6f1fa8ddc56a42c5ec64e1bf54fb972d718a91b17ab0ad9533fa14312c41fd0","observation_id":"3e73ec52-4d83-4afe-9281-ba2afc1ac186","resolution":{"observed_at":"2026-08-14T12:39:39.508439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.489296Z","title":"Temporal graph offset reconstruction: Towards tempo- rally robust graph representation learning,","venue":null,"work_id":"afa4ed91-0900-466a-a730-ac45505bcbf4","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.098443Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:b90b680c5ff2f37242c5ece16c3ea80440a70c5001d3436fe032cd437a33202a","observation_id":"610f0bb6-f34c-4214-9d45-c6ce9825e33c","resolution":{"observed_at":"2026-08-14T12:39:39.494666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.474948Z","title":"One-shot learning of object categories,","venue":null,"work_id":"46be3112-2e04-4af3-9e28-1070744be529","year":2006},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.102700Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:69c20967e37f903d3cf288e39cc24f83c3378076616085fd791e9d33571342cd","observation_id":"8e8a106d-55e1-433c-925d-c9e962c2ed64","resolution":{"observed_at":"2026-08-14T12:39:39.479255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.462259Z","title":"One-shot learning of generative speech concepts,","venue":null,"work_id":"1e653612-034c-48a2-b0c7-a7008896322a","year":2014},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.106979Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:055afa7adbabda2cf199f38d6120c7fc0dccfb411c9b5d6aa8e0374eb49d0ab5","observation_id":"3ac1c374-b925-402d-a690-4765abbd26dc","resolution":{"observed_at":"2026-08-14T12:39:39.466381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.449070Z","title":"Siamese neural networks for one-shot image recognition,","venue":null,"work_id":"a6697d79-97e6-4827-ac4d-3f122820a6e3","year":2015},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.110863Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:5f3593f20bacdf3227a6e38fb9834d8d1468bb7608b0f36b4783b1fdd179f6c0","observation_id":"a0d552d8-a077-4b25-b0a9-fe9d5906c423","resolution":{"observed_at":"2026-08-14T12:39:39.454115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.06065","last_updated":"2016-05-19T17:44:51Z","snapshot_observed_at":"2026-08-14T21:56:38.050895Z","submitted_at":"2016-05-19T17:44:51Z","title":"One-shot Learning with Memory-Augmented Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.06065","snapshot_observed_at":"2026-08-14T12:39:39.114563Z","title":"One-shot learning with memory-augmented neural networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.114563Z"},"links":{"cited_paper":"/paper/1605.06065","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:8d7f5a0ac94d5fb425148ab04b703f8fe25e48bbc555c06ddd5d88f7f75ca66e","observation_id":"36e9663c-1b60-49ba-bd54-4bfd599a6dab","resolution":{"observed_at":"2026-08-14T12:39:39.114563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.437515Z","title":"Matching networks for one shot learning,","venue":null,"work_id":"447982a3-2edf-4fdd-8be7-1b8b61d0c6bb","year":2016},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.119612Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:67c13f73219b81587c091e05cf8b10114703526a90a9011703629e2d6debd447","observation_id":"d72d3d75-644e-401b-939c-8003bd2e0d4e","resolution":{"observed_at":"2026-08-14T12:39:39.441602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.425232Z","title":"Multi- level semantic feature augmentation for one-shot learning,","venue":null,"work_id":"8193e7ea-76ad-4873-9b0d-a2cdefbcad42","year":2019},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.123598Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:a664cc2e48a084b32672f0c1331816a6fe1d31d26b8f62a5c02e4e8dbd14b8f2","observation_id":"f8665759-6323-45d8-bd6b-5bf59021c72c","resolution":{"observed_at":"2026-08-14T12:39:39.429709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.412726Z","title":"Data augmentation using learned transformations for one-shot medical image segmentation,","venue":null,"work_id":"5f19c397-3c70-492d-a284-f4a38c93e698","year":2019},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.128361Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:a798d6681f14e78a675183574b069bccf1de24d1e7013225a6c8825774bf481a","observation_id":"b4c2ef0b-9ae5-4d35-a4fd-872e9f9dec60","resolution":{"observed_at":"2026-08-14T12:39:39.417086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.399567Z","title":"Dropout inference in Bayesian networks with alpha- divergences,","venue":null,"work_id":"270f679c-7518-4df2-a618-75144af7e2ef","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.132750Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:73751792f3770db5259ef555dba7587382b76cfd8b5fc004b214b80b01afa78b","observation_id":"c2799f6f-15b5-43d7-bdb2-897e4e3efd0b","resolution":{"observed_at":"2026-08-14T12:39:39.403924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.385288Z","title":"Neural network based intrusion detection system for critical infrastructures,","venue":null,"work_id":"ac201223-0794-4cb6-b941-0b05be315ac0","year":2009},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.137199Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:7bdd444469f9795252451fc15442d972184364e6d8cf246c57bdd04ab122559e","observation_id":"e29f3a8a-d338-49c0-a287-32a4f632dadc","resolution":{"observed_at":"2026-08-14T12:39:39.389917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.370903Z","title":"What uncertainties do we need in Bayesian deep learning for computer vision?","venue":null,"work_id":"7f6f3744-cecd-47e8-9215-6d774eb5c189","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.142441Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:40213b9da6a26d81e1ff9e428c8cc0c4938f7f9735602cb72b35c9d41e75494f","observation_id":"51f6dc0d-5398-46e5-ba7a-c5961b9666f8","resolution":{"observed_at":"2026-08-14T12:39:39.376350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.356996Z","title":"Dropout: A simple way to prevent neural networks from overﬁtting,","venue":null,"work_id":"9cb1037e-7214-49a6-83b3-c7aeb38d31ca","year":1929},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.147753Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:ef0c2c1519dfa1c4749c4c35dffd6e86bd230a94ac1ac4751f445385abd8e1a6","observation_id":"f8bca989-d789-48e6-8d66-93ebc49dea29","resolution":{"observed_at":"2026-08-14T12:39:39.361439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.343866Z","title":"Deep Gaussian processes,","venue":null,"work_id":"cbed8146-08a6-4bf5-b353-fcd2ef4e6a7a","year":2013},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.151776Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:e167e612fc749d72b22776a0340180d36aab88c1c30601eb3d7421c954f6edec","observation_id":"5891346b-f864-4a08-a982-8f8feaec8d9a","resolution":{"observed_at":"2026-08-14T12:39:39.348587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.330286Z","title":"Automatic differentiation in PyTorch,","venue":null,"work_id":"1ead2662-b8be-46b6-8eb3-835e6665afc1","year":2017},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.155673Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:f143f7d1cb2b77005c2980f25a8686fa902e2a4b9fa5518a28cb18089fdca452","observation_id":"3aa7bc20-cb6f-4de1-abea-990fe67619dd","resolution":{"observed_at":"2026-08-14T12:39:39.334608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.314477Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":"1d1c1a4b-7dd8-42a3-8b00-2a419c56967e","year":2014},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.160670Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:877c9bb5bcb45504bb7015e033a3b72b6d561084a79a8bb22446b91f0774408e","observation_id":"3588ff1f-3a22-474e-9057-e8d11f2d667b","resolution":{"observed_at":"2026-08-14T12:39:39.318912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.03973","last_updated":"2019-03-05T23:11:16Z","snapshot_observed_at":"2026-08-20T17:13:31.899554Z","submitted_at":"2018-12-10T18:46:21Z","title":"Bayesian Layers: A Module for Neural Network Uncertainty","version":3},"cited_work":{"arxiv_id":"1812.03973","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.03973","snapshot_observed_at":"2026-08-14T12:39:39.203537Z","title":"Bayesian Layers: A Module for Neural Network Uncertainty","venue":"cs.LG","work_id":"166ee294-1f85-4e50-991d-10c72dfb62e3","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.164542Z"},"links":{"cited_paper":"/paper/1812.03973","citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:e462b13bb66a79bc1fbf28f43ce47c657663ec9f18db7f6f5da4aed3e12b088a","observation_id":"5144f906-3c4f-494d-bd9c-e3f95987a44a","resolution":{"observed_at":"2026-08-14T12:39:39.209871Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:39:39.301393Z","title":"Optimizing over a Bayesian last layer,","venue":null,"work_id":"81c146d3-3cfc-441c-9f4d-b39ff7ac4059","year":2018},"citing_paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T12:39:39.168929Z"},"links":{"citing_paper":"/paper/1908.06750"},"observation_digest":"sha256:9def4f98d3b2ea25ad22a188da1d144002ab38e8c6aca5d09cfc451d228427d9","observation_id":"9581a8cc-8260-4873-b616-5744420bd344","resolution":{"observed_at":"2026-08-14T12:39:39.305606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.06750","last_updated":"2019-08-19T12:38:38Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-21T03:09:04.548053Z","submitted_at":"2019-08-19T12:38:38Z","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":2,"verified_fuzzy":43},"total_outbound_references":51},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:1908.06750."}